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mammogram</title> <meta name="description" content="Search results for: mammogram"> <meta name="keywords" content="mammogram"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="mammogram"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 27</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: mammogram</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27</span> Effective Texture Features for Segmented Mammogram Images Based on Multi-Region of Interest Segmentation Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramayanam%20Suresh">Ramayanam Suresh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Nagaraja%20Rao"> A. Nagaraja Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Eswara%20Reddy"> B. Eswara Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Texture features of mammogram images are useful for finding masses or cancer cases in mammography, which have been used by radiologists. Textures are greatly succeeded for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) is most commonly used technique for mammogram segmentation. Limitation of this method is that it is unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly in finding the best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=texture%20features" title="texture features">texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20of%20interest" title=" region of interest"> region of interest</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-ROI%20segmentation" title=" multi-ROI segmentation"> multi-ROI segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=benchmarked%20images" title=" benchmarked images "> benchmarked images </a> </p> <a href="https://publications.waset.org/abstracts/88666/effective-texture-features-for-segmented-mammogram-images-based-on-multi-region-of-interest-segmentation-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88666.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">310</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">26</span> Detection and Classification of Mammogram Images Using Principle Component Analysis and Lazy Classifiers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajkumar%20Kolangarakandy">Rajkumar Kolangarakandy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PCA" title="PCA">PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transformation" title=" wavelet transformation"> wavelet transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=lazy%20classifiers" title=" lazy classifiers"> lazy classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=Kstar" title=" Kstar"> Kstar</a>, <a href="https://publications.waset.org/abstracts/search?q=IBL" title=" IBL"> IBL</a>, <a href="https://publications.waset.org/abstracts/search?q=LWL" title=" LWL"> LWL</a> </p> <a href="https://publications.waset.org/abstracts/36911/detection-and-classification-of-mammogram-images-using-principle-component-analysis-and-lazy-classifiers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36911.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">335</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25</span> Computer Aided Classification of Architectural Distortion in Mammograms Using Texture Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Birmohan%20Singh">Birmohan Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=V.K.Jain"> V.K.Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=architecture%20distortion" title="architecture distortion">architecture distortion</a>, <a href="https://publications.waset.org/abstracts/search?q=mammograms" title=" mammograms"> mammograms</a>, <a href="https://publications.waset.org/abstracts/search?q=GLCM%20texture%20features" title=" GLCM texture features"> GLCM texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=GLRLM%20texture%20features" title=" GLRLM texture features"> GLRLM texture features</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20classifier" title=" support vector machine classifier"> support vector machine classifier</a> </p> <a href="https://publications.waset.org/abstracts/29952/computer-aided-classification-of-architectural-distortion-in-mammograms-using-texture-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29952.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">491</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">24</span> Lacunarity measures on Mammographic Image Applying Fractal Dimension and Lacunarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Sushma">S. Sushma</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Balasubramanian"> S. Balasubramanian</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20C.%20Latha"> K. C. Latha</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Sridhar"> R. Sridhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structural texture measures are used to address the aspect of breast cancer risk assessment in screening mammograms. The current study investigates whether texture properties characterized by local Fractal Dimension (FD) and lacunarity contribute to assess breast cancer risk. Fractal Dimension represents the complexity while the lacunarity characterize the gap of a fractal dimension. In this paper, we present our result confirming that the lacunarity value resulted in algorithm using mammogram images states that level of lacunarity will be low when the Fractal Dimension value will be high. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20analysis" title=" image analysis"> image analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=lacunarity" title=" lacunarity"> lacunarity</a>, <a href="https://publications.waset.org/abstracts/search?q=mammogram" title=" mammogram"> mammogram</a> </p> <a href="https://publications.waset.org/abstracts/13593/lacunarity-measures-on-mammographic-image-applying-fractal-dimension-and-lacunarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">389</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">23</span> Least-Square Support Vector Machine for Characterization of Clusters of Microcalcifications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Baljit%20Singh%20Khehra">Baljit Singh Khehra</a>, <a href="https://publications.waset.org/abstracts/search?q=Amar%20Partap%20Singh%20Pharwaha"> Amar Partap Singh Pharwaha </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clusters%20of%20microcalcifications" title="clusters of microcalcifications">clusters of microcalcifications</a>, <a href="https://publications.waset.org/abstracts/search?q=ductal%20carcinoma%20in%20situ" title=" ductal carcinoma in situ"> ductal carcinoma in situ</a>, <a href="https://publications.waset.org/abstracts/search?q=least-square%20support%20vector%20machine" title=" least-square support vector machine"> least-square support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/3186/least-square-support-vector-machine-for-characterization-of-clusters-of-microcalcifications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3186.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22</span> A Review of Deep Learning Methods in Computer-Aided Detection and Diagnosis Systems based on Whole Mammogram and Ultrasound Scan Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ian%20Omung%27a">Ian Omung'a</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20diagnosis" title="breast cancer diagnosis">breast cancer diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20aided%20detection%20and%20diagnosis" title=" computer aided detection and diagnosis"> computer aided detection and diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=whole%20mammogram%20classfication" title=" whole mammogram classfication"> whole mammogram classfication</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20classification" title=" ultrasound classification"> ultrasound classification</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/148925/a-review-of-deep-learning-methods-in-computer-aided-detection-and-diagnosis-systems-based-on-whole-mammogram-and-ultrasound-scan-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148925.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21</span> An Approach Based on Statistics and Multi-Resolution Representation to Classify Mammograms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nebi%20Gedik">Nebi Gedik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wave%20atom%20transform" title="wave atom transform">wave atom transform</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20features" title=" statistical features"> statistical features</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-resolution%20representation" title=" multi-resolution representation"> multi-resolution representation</a>, <a href="https://publications.waset.org/abstracts/search?q=mammogram" title=" mammogram"> mammogram</a> </p> <a href="https://publications.waset.org/abstracts/62356/an-approach-based-on-statistics-and-multi-resolution-representation-to-classify-mammograms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62356.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20</span> Convolutional Neural Networks versus Radiomic Analysis for Classification of Breast Mammogram</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehwish%20Asghar">Mehwish Asghar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20%28BC%29" title="breast cancer (BC)">breast cancer (BC)</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20%28ML%29" title=" machine learning (ML)"> machine learning (ML)</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network%20%28CNN%29" title=" convolutional neural network (CNN)"> convolutional neural network (CNN)</a>, <a href="https://publications.waset.org/abstracts/search?q=radionics" title=" radionics"> radionics</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/143691/convolutional-neural-networks-versus-radiomic-analysis-for-classification-of-breast-mammogram" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143691.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">225</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">19</span> 99mTc Scintimammography in an Equivocal Breast Lesion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malak%20Shawky%20Matter%20Elyas">Malak Shawky Matter Elyas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Early detection of breast cancer is the main tool to decrease morbidity and mortality rates. Many diagnostic tools are used, such as mammograms, ultrasound and magnetic resonance imaging, but none of them is conclusive, especially in very small sizes, less than 1 cm. So, there is a need for more accurate tools. Patients and methods: This study involved 13 patients with different breast lesions. 6 Patients had breast cancer, and one of them had metastatic axillary lymph nodes without clinically nor mammographically detected breast mass proved by biopsy and histopathology. Of the other 7 Patients, 4 of them had benign breast lesions proved by biopsy and histopathology, and 3 Patients showed Equivocal breast lesions on a mammogram. A volume of 370-444Mbq of (99m) Tc/ bombesin was injected. Dynamic 1-min images by Gamma Camera were taken for 20 minutes immediately after injection in the anterior view. Thereafter, two static images in anterior and prone lateral views by Gamma Camera were taken for 5 minutes. Finally, single-photon emission computed tomography images were taken for each patient. The definitive diagnosis was based on biopsy and histopathology. Results: 6 Patients with breast cancer proved by biopsy and histopathology showed Positive findings on Sestamibi (Scintimammography). 1 out of 4 Patients with benign breast lesions proved by biopsy and histopathology showed Positive findings on Sestamibi (Scintimammography) while the other 3 Patients showed Negative findings on Sestamibi. 3 Patients out of 3 Patients with equivocal breast findings on mammogram showed Positive Findings on Sestamibi (Scintimammography) and proved by biopsy and histopathology. Conclusions: While we agree that Scintimammography will not replace mammograms as a mass screening tool, we believe that many patients will benefit from Scintimammography, especially women with dense breast tissues and in the presence of breast implants that are difficult to diagnose by mammogram, wherein its sensitivity is low and in women with metastatic axillary lymph nodes without clinically nor mammographically findings. We can use Scintimammography in sentinel lymph node mapping as a more accurate tool, especially since it is non-invasive. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast." title="breast.">breast.</a>, <a href="https://publications.waset.org/abstracts/search?q=radiodiagnosis" title=" radiodiagnosis"> radiodiagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=lifestyle" title=" lifestyle"> lifestyle</a>, <a href="https://publications.waset.org/abstracts/search?q=surgery" title=" surgery"> surgery</a> </p> <a href="https://publications.waset.org/abstracts/189033/99mtc-scintimammography-in-an-equivocal-breast-lesion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189033.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">31</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18</span> Automated Digital Mammogram Segmentation Using Dispersed Region Growing and Pectoral Muscle Sliding Window Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayush%20Shrivastava">Ayush Shrivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Arpit%20Chaudhary"> Arpit Chaudhary</a>, <a href="https://publications.waset.org/abstracts/search?q=Devang%20Kulshreshtha"> Devang Kulshreshtha</a>, <a href="https://publications.waset.org/abstracts/search?q=Vibhav%20Prakash%20Singh"> Vibhav Prakash Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajeev%20Srivastava"> Rajeev Srivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Early diagnosis of breast cancer can improve the survival rate by detecting cancer at an early stage. Breast region segmentation is an essential step in the analysis of digital mammograms. Accurate image segmentation leads to better detection of cancer. It aims at separating out Region of Interest (ROI) from rest of the image. The procedure begins with removal of labels, annotations and tags from the mammographic image using morphological opening method. Pectoral Muscle Sliding Window Algorithm (PMSWA) is used for removal of pectoral muscle from mammograms which is necessary as the intensity values of pectoral muscles are similar to that of ROI which makes it difficult to separate out. After removing the pectoral muscle, Dispersed Region Growing Algorithm (DRGA) is used for segmentation of mammogram which disperses seeds in different regions instead of a single bright region. To demonstrate the validity of our segmentation method, 322 mammographic images from Mammographic Image Analysis Society (MIAS) database are used. The dataset contains medio-lateral oblique (MLO) view of mammograms. Experimental results on MIAS dataset show the effectiveness of our proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CAD" title="CAD">CAD</a>, <a href="https://publications.waset.org/abstracts/search?q=dispersed%20region%20growing%20algorithm%20%28DRGA%29" title=" dispersed region growing algorithm (DRGA)"> dispersed region growing algorithm (DRGA)</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=pectoral%20muscle%20sliding%20window%20algorithm%20%28PMSWA%29" title=" pectoral muscle sliding window algorithm (PMSWA)"> pectoral muscle sliding window algorithm (PMSWA)</a> </p> <a href="https://publications.waset.org/abstracts/69020/automated-digital-mammogram-segmentation-using-dispersed-region-growing-and-pectoral-muscle-sliding-window-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69020.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">312</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17</span> Aspects and Studies of Fractal Geometry in Automatic Breast Cancer Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mrinal%20Kanti%20Bhowmik">Mrinal Kanti Bhowmik</a>, <a href="https://publications.waset.org/abstracts/search?q=Kakali%20Das%20Jr."> Kakali Das Jr.</a>, <a href="https://publications.waset.org/abstracts/search?q=Barin%20Kumar%20De"> Barin Kumar De</a>, <a href="https://publications.waset.org/abstracts/search?q=Debotosh%20Bhattacharjee"> Debotosh Bhattacharjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer is the most common cancer and a leading cause of death for women in the 35 to 55 age group. Early detection of breast cancer can decrease the mortality rate of breast cancer. Mammography is considered as a ‘Gold Standard’ for breast cancer detection and a very popular modality, presently used for breast cancer screening and detection. The screening of digital mammograms often leads to over diagnosis and a consequence to unnecessary traumatic & painful biopsies. For that reason recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. Tumor is a significant sign of breast cancer in both mammography and thermography. The tumors are complex in structure and they also exhibit a different statistical and textural features compared to the breast background tissue. Fractal geometry is a geometry which is used to describe this type of complex structure as per their main characteristic, where traditional Euclidean geometry fails. Over the last few years, fractal geometrics have been applied mostly in many medical image (1D, 2D, or 3D) analysis applications. In breast cancer detection using digital mammogram images, also it plays a significant role. Fractal is also used in thermography for early detection of the masses using the thermal texture. This paper presents an overview of the recent aspects and initiatives of fractals in breast cancer detection in both mammography and thermography. The scope of fractal geometry in automatic breast cancer detection using digital mammogram and thermogram images are analysed, which forms a foundation for further study on application of fractal geometry in medical imaging for improving the efficiency of automatic detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal" title="fractal">fractal</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor" title=" tumor"> tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=thermography" title=" thermography"> thermography</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a> </p> <a href="https://publications.waset.org/abstracts/22188/aspects-and-studies-of-fractal-geometry-in-automatic-breast-cancer-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22188.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">388</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16</span> Mammographic Multi-View Cancer Identification Using Siamese Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alisher%20Ibragimov">Alisher Ibragimov</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofya%20Senotrusova"> Sofya Senotrusova</a>, <a href="https://publications.waset.org/abstracts/search?q=Aleksandra%20Beliaeva"> Aleksandra Beliaeva</a>, <a href="https://publications.waset.org/abstracts/search?q=Egor%20Ushakov"> Egor Ushakov</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuri%20Markin"> Yuri Markin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20diagnosis" title=" computer-aided diagnosis"> computer-aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-view%20mammogram" title=" multi-view mammogram"> multi-view mammogram</a>, <a href="https://publications.waset.org/abstracts/search?q=siamese%20neural%20network" title=" siamese neural network"> siamese neural network</a> </p> <a href="https://publications.waset.org/abstracts/173794/mammographic-multi-view-cancer-identification-using-siamese-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173794.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">15</span> Understanding Jordanian Women's Values and Beliefs Related to Prevention and Early Detection of Breast Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khlood%20F.%20Salman">Khlood F. Salman</a>, <a href="https://publications.waset.org/abstracts/search?q=Richard%20Zoucha"> Richard Zoucha</a>, <a href="https://publications.waset.org/abstracts/search?q=Hani%20Nawafleh"> Hani Nawafleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Jordan ranks the fourth highest breast cancer prevalence after Lebanon, Bahrain, and Kuwait. Considerable evidence showed that cultural, ethnic, and economic differences influence a woman’s practice to early detection and prevention of breast cancer. Objectives: To understand women’s health beliefs and values in relation to early detection of breast cancer; and to explore the impact of these beliefs on their decisions regarding reluctance or acceptance of early detection measures such as mammogram screening. Design: A qualitative focused ethnography was used to collect data for this study. Settings: The study was conducted in the second largest city surrounded by a large rural area in Ma’an- Jordan. Participants: A total of twenty seven women, with no history of breast cancer, between the ages of 18 and older, who had prior health experience with health providers, and were willing to share elements of personal health beliefs related to breast health within the larger cultural context. The participants were recruited using the snowball method and words of mouth. Data collection and analysis: A short questionnaire was designed to collect data related to socio demographic status (SDQ) from all participants. A Semi-structured interviews guide was used to elicit data through interviews with the informants. Nvivo10 a data manager was utilized to assist with data analysis. Leininger’s four phases of qualitative data analysis was used as a guide for the data analysis. The phases used to analyze the data included: 1) Collecting and documenting raw data, 2) Identifying of descriptors and categories according to the domains of inquiry and research questions. Emic and etic data is coded for similarities and differences, 3) Identifying patterns and contextual analysis, discover saturation of ideas and recurrent patterns, and 4) Identifying themes and theoretical formulations and recommendations. Findings: Three major themes were emerged within the cultural and religious context; 1. Fear, denial, embarrassment and lack of knowledge were common perceptions of Ma’anis’ women regarding breast health and screening mammography, 2. Health care professionals in Jordan were not quick to offer information and education about breast cancer and screening, and 3. Willingness to learn about breast health and cancer prevention. Conclusion: The study indicated the disparities between the infrastructure and resourcing in rural and urban areas of Jordan, knowledge deficit related to breast cancer, and lack of education about breast health may impact women’s decision to go for a mammogram screening. Cultural beliefs, fear, embarrassments as well as providers lack of focus on breast health were significant contributors against practicing breast health. Health providers and policy makers should provide resources for the establishment health education programs regarding breast cancer early detection and mammography screening. Nurses should play a major role in delivering health education about breast health in general and breast cancer in particular. A culturally appropriate health awareness messages can be used in creating educational programs which can be employed at the national levels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20health" title="breast health">breast health</a>, <a href="https://publications.waset.org/abstracts/search?q=beliefs" title=" beliefs"> beliefs</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20context" title=" cultural context"> cultural context</a>, <a href="https://publications.waset.org/abstracts/search?q=ethnography" title=" ethnography"> ethnography</a>, <a href="https://publications.waset.org/abstracts/search?q=mammogram%20screening" title=" mammogram screening"> mammogram screening</a> </p> <a href="https://publications.waset.org/abstracts/35533/understanding-jordanian-womens-values-and-beliefs-related-to-prevention-and-early-detection-of-breast-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35533.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">298</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">14</span> Content-Based Mammograms Retrieval Based on Breast Density Criteria Using Bidimensional Empirical Mode Decomposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sourour%20Khouaja">Sourour Khouaja</a>, <a href="https://publications.waset.org/abstracts/search?q=Hejer%20Jlassi"> Hejer Jlassi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Feddaoui"> Nadia Feddaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Hamrouni"> Kamel Hamrouni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most medical images, and especially mammographies, are now stored in large databases. Retrieving a desired image is considered of great importance in order to find previous similar cases diagnosis. Our method is implemented to assist radiologists in retrieving mammographic images containing breast with similar density aspect as seen on the mammogram. This is becoming a challenge seeing the importance of density criteria in cancer provision and its effect on segmentation issues. We used the BEMD (Bidimensional Empirical Mode Decomposition) to characterize the content of images and Euclidean distance measure similarity between images. Through the experiments on the MIAS mammography image database, we confirm that the results are promising. The performance was evaluated using precision and recall curves comparing query and retrieved images. Computing recall-precision proved the effectiveness of applying the CBIR in the large mammographic image databases. We found a precision of 91.2% for mammography with a recall of 86.8%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BEMD" title="BEMD">BEMD</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20density" title=" breast density"> breast density</a>, <a href="https://publications.waset.org/abstracts/search?q=contend-based" title=" contend-based"> contend-based</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a> </p> <a href="https://publications.waset.org/abstracts/59187/content-based-mammograms-retrieval-based-on-breast-density-criteria-using-bidimensional-empirical-mode-decomposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59187.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">232</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">13</span> Computer Aided Analysis of Breast Based Diagnostic Problems from Mammograms Using Image Processing and Deep Learning Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Berkan%20Ural">Ali Berkan Ural</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20aided%20diagnosis" title="computer aided diagnosis">computer aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20growing" title=" region growing"> region growing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/155700/computer-aided-analysis-of-breast-based-diagnostic-problems-from-mammograms-using-image-processing-and-deep-learning-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155700.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12</span> How Group Education Impacts Female Factory Workers’ Behavior and Readiness to Receive Mammography and Pap Smears</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Memnun%20Seven">Memnun Seven</a>, <a href="https://publications.waset.org/abstracts/search?q=Mine%20Bahar"> Mine Bahar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayg%C3%BCl%20Aky%C3%BCz"> Aygül Akyüz</a>, <a href="https://publications.waset.org/abstracts/search?q=Hatice%20Erdo%C4%9Fan"> Hatice Erdoğan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The workplace has been deemed a suitable location for educating many women at once about cancer screening. Objective: To determine how group education about early diagnostic methods for breast and cervical cancer affects women’s behavior and readiness to receive mammography and Pap smears. Methods: This semi-interventional study was conducted at a textile factory in Istanbul, Turkey. Female workers (n = 125) were included in the study. A participant identification form and knowledge evaluation form developed for this study, along with the trans-theoretical model, were used to collect data. A 45-min interactive group education was given to the participants. Results: Upon contacting participants 3 months after group education, 15.4% (n = 11) stated that they had since received a mammogram and 9.8% (n = 7) a Pap smear. As suggested by the trans-theoretical model, group education increased participants’ readiness to receive cancer screening, along with their knowledge of breast and cervical cancer. Conclusions: Group education positively impacted women’s knowledge of cancer and their readiness to receive mammography and Pap smears. Group education can therefore potentially create awareness of cancer screening tests among women and improve their readiness to receive such tests. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20screening" title="cancer screening">cancer screening</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20intervention" title=" educational intervention"> educational intervention</a>, <a href="https://publications.waset.org/abstracts/search?q=participation" title=" participation"> participation</a>, <a href="https://publications.waset.org/abstracts/search?q=women" title=" women "> women </a> </p> <a href="https://publications.waset.org/abstracts/16775/how-group-education-impacts-female-factory-workers-behavior-and-readiness-to-receive-mammography-and-pap-smears" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16775.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">329</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> Deep Feature Augmentation with Generative Adversarial Networks for Class Imbalance Learning in Medical Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rongbo%20Shen">Rongbo Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianhua%20Yao"> Jianhua Yao</a>, <a href="https://publications.waset.org/abstracts/search?q=Kezhou%20Yan"> Kezhou Yan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuan%20Tian"> Kuan Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Jiang"> Cheng Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ke%20Zhou"> Ke Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=class%20imbalance" title="class imbalance">class imbalance</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20sampling" title=" synthetic sampling"> synthetic sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20augmentation" title=" feature augmentation"> feature augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20cleaning" title=" data cleaning"> data cleaning</a> </p> <a href="https://publications.waset.org/abstracts/114272/deep-feature-augmentation-with-generative-adversarial-networks-for-class-imbalance-learning-in-medical-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114272.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10</span> Wire Localization Procedures in Non-Palpable Breast Cancers: An Audit Report and Review of Literature</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Waqas%20Ahmad">Waqas Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Eisha%20Tahir"> Eisha Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahper%20Aqeel"> Shahper Aqeel</a>, <a href="https://publications.waset.org/abstracts/search?q=Imran%20Khalid%20Niazi"> Imran Khalid Niazi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjad%20Iqbal"> Amjad Iqbal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Breast conservation surgery applies a number of techniques for accurate localization of lesions. Wire localization remains the method of choice in non-palpable breast cancers post-neoadjuvant chemotherapy. Objective: The aim of our study was to determine the accuracy of wire localization procedures in our department and compare it with internationally set protocols as per the Royal College of Radiologists. Post wire mammography, as well as the margin status of the postoperative specimen, assessed the accuracy of the procedure. Methods: We retrospectively reviewed the data of 225 patients who presented to our department from May 2014 to June 2015 post neoadjuvant chemotherapy with non-palpable cancers. These patients are candidates for wire localized lumpectomies either under ultrasound or stereotactic guidance. Metallic marker was placed in all the patients at the time of biopsy. Post wire mammogram was performed in all the patients and the distance of the wire tip from the marker was calculated. The presence or absence of the metallic clip in the postoperative specimen, as well as the marginal status of the postoperative specimen, was noted. Results: 157 sonographic and 68 stereotactic wire localization procedures were performed. 95% of the wire tips were within 1 cm of the metallic marker. Marginal status was negative in 94% of the patients in histopathological specimen. Conclusion: Our audit report declares more than 95% accuracy of image guided wire localization in successful excision of non-palpable breast lesions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast" title="breast">breast</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer" title=" cancer"> cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=non-palpable" title=" non-palpable"> non-palpable</a>, <a href="https://publications.waset.org/abstracts/search?q=wire%20localization" title=" wire localization"> wire localization</a> </p> <a href="https://publications.waset.org/abstracts/49198/wire-localization-procedures-in-non-palpable-breast-cancers-an-audit-report-and-review-of-literature" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49198.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">308</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9</span> Factors Associated with Mammography Screening Behaviors: A Cross-Sectional Descriptive Study of Egyptian Women </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salwa%20Hagag%20Abdelaziz">Salwa Hagag Abdelaziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Naglaa%20Fathy%20Youssef"> Naglaa Fathy Youssef</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Abdellatif%20Hassan"> Nadia Abdellatif Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Rasha%20Wesam%20Abdelrahman"> Rasha Wesam Abdelrahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast cancer is considered as a substantial health concern and practicing mammography screening [MS] is important in minimizing its related morbidity. So it is essential to have a better understanding of breast cancer screening behaviors of women and factors that influence utilization of them. The aim of this study is to identify the factors that are linked to MS behaviors among the Egyptian women. A cross-sectional descriptive design was carried out to provide a snapshot of the factors that are linked to MS behaviors. A convenience sample of 311 women was utilized and all eligible participants admitted to the Women Imaging Unit who are 40 years of age or above, coming for mammography assessment, not pregnant or breast feeding and who accepted to participate in the study were included. A structured questionnaire was developed by the researchers and contains three parts; Socio-demographic data; Motivating factors associated with MS; and association between MS and model of behavior change. The analyzed data indicated that most of the participated women (66.6 %) belonged to the age group of 40-49.A high proportion of participants (58.1%) of group having previous MS influenced by their neighbors to practice MS, whereas 32.7 % in group not having previous MS were influenced by family members which indicated significant differences (P <0.05). Doctors and media are shown to be the least influence of others to practice MS. Women with intention to have a future mammogram had higher OR (1.404) for practicing MS compared with women with no intention. Further studies are needed to examine the relation between Trans-theoretical Model [TTM] and practicing MS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=screening%20behaviors" title=" screening behaviors"> screening behaviors</a>, <a href="https://publications.waset.org/abstracts/search?q=morbidity" title=" morbidity"> morbidity</a> </p> <a href="https://publications.waset.org/abstracts/28433/factors-associated-with-mammography-screening-behaviors-a-cross-sectional-descriptive-study-of-egyptian-women" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28433.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">442</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> Diagnostic Efficacy and Usefulness of Digital Breast Tomosynthesis (DBT) in Evaluation of Breast Microcalcifications as a Pre-Procedural Study for Stereotactic Biopsy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Okhee%20Woo">Okhee Woo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hye%20Seon%20Shin"> Hye Seon Shin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: To investigate the diagnostic power of digital breast tomosynthesis (DBT) in evaluation of breast microcalcifications and usefulness as a pre-procedural study for stereotactic biopsy in comparison with full-field digital mammogram (FFDM) and FFDM plus magnification image (FFDM+MAG). Methods and Materials: An IRB approved retrospective observer performance study on DBT, FFDM, and FFDM+MAG was done. Image quality was rated in 5-point scoring system for lesion clarity (1, very indistinct; 2, indistinct; 3, fair; 4, clear; 5, very clear) and compared by Wilcoxon test. Diagnostic power was compared by diagnostic values and AUC with 95% confidence interval. Additionally, procedural report of biopsy was analysed for patient positioning and adequacy of instruments. Results: DBT showed higher lesion clarity (median 5, interquartile range 4-5) than FFDM (3, 2-4, p-value < 0.0001), and no statistically significant difference to FFDM+MAG (4, 4-5, p-value=0.3345). Diagnostic sensitivity and specificity of DBT were 86.4% and 92.5%; FFDM 70.4% and 66.7%; FFDM+MAG 93.8% and 89.6%. The AUCs of DBT (0.88) and FFDM+MAG (0.89) were larger than FFDM (0.59, p-values < 0.0001) but there was no statistically significant difference between DBT and FFDM+MAG (p-value=0.878). In 2 cases with DBT, petit needle could be appropriately prepared; and other 3 without DBT, patient repositioning was needed. Conclusion: DBT showed better image quality and diagnostic values than FFDM and equivalent to FFDM+MAG in the evaluation of breast microcalcifications. Evaluation with DBT as a pre-procedural study for breast stereotactic biopsy can lead to more accurate localization and successful biopsy and also waive the need for additional magnification images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DBT" title="DBT">DBT</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=stereotactic%20biopsy" title=" stereotactic biopsy"> stereotactic biopsy</a>, <a href="https://publications.waset.org/abstracts/search?q=mammography" title=" mammography"> mammography</a> </p> <a href="https://publications.waset.org/abstracts/82986/diagnostic-efficacy-and-usefulness-of-digital-breast-tomosynthesis-dbt-in-evaluation-of-breast-microcalcifications-as-a-pre-procedural-study-for-stereotactic-biopsy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82986.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">304</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> An Audit of Local Guidance Compliance For Stereotactic Core Biopsy For DCIS In The Breast Screening Programme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aisling%20Eves">Aisling Eves</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Pieri"> Andrew Pieri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ross%20McLean"> Ross McLean</a>, <a href="https://publications.waset.org/abstracts/search?q=Nerys%20Forester"> Nerys Forester</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The breast unit local guideline recommends that 12 cores should be used in a stereotactic-guided biopsy to diagnose DCIS. Twelve cores are regarded to provide good diagnostic value without removing more breast tissue than necessary. This study aimed to determine compliance with guidelines and investigated how the number of cores impacted upon the re-excision rate and size discrepancies. Methods: This single-centre retrospective cohort study of 72 consecutive breast screened patients with <15mm DCIS on radiological report underwent stereotactic-guided core biopsy and subsequent surgical excision. Clinical, radiological, and histological data were collected over 5 years, and ASCO guidelines for margin involvement of <2mm was used to guide the need for re-excision. Results: Forty-six (63.9%) patients had <12 cores taken, and 26 (36.1%) patients had ≥12 cores taken. Only six (8.3%) patients had 12 cores taken in their stereotactic biopsy. Incomplete surgical excision was seen in 17 patients overall (23.6%), and of these patients, twelve (70.6%) had fewer than 12 cores taken (p=0.55 for the difference between groups). Mammogram and biopsy underestimated the size of the DCIS in this subgroup by a median of 15mm (range: 6-135mm). Re-excision was required in 9 patients (12.5%), and five patients (6.9%) were found to have invasive ductal carcinoma on excision (80% had <12 cores, p=0.43). Discussion: There is poor compliance with the breast unit local guidelines and higher rates of re-excision in patients who did not have ≥12 cores taken. Taking ≥12 cores resulted in fewer missed invasive cancers lower incomplete excision and re-excision rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stereotactic%20core%20biopsy" title="stereotactic core biopsy">stereotactic core biopsy</a>, <a href="https://publications.waset.org/abstracts/search?q=DCIS" title=" DCIS"> DCIS</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20screening" title=" breast screening"> breast screening</a>, <a href="https://publications.waset.org/abstracts/search?q=Re-excision%20rates" title=" Re-excision rates"> Re-excision rates</a>, <a href="https://publications.waset.org/abstracts/search?q=core%20biopsy" title=" core biopsy"> core biopsy</a> </p> <a href="https://publications.waset.org/abstracts/146029/an-audit-of-local-guidance-compliance-for-stereotactic-core-biopsy-for-dcis-in-the-breast-screening-programme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146029.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Comparison of Radiation Dosage and Image Quality: Digital Breast Tomosynthesis vs. Full-Field Digital Mammography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Okhee%20Woo">Okhee Woo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: With increasing concern of individual radiation exposure doses, studies analyzing radiation dosage in breast imaging modalities are required. Aim of this study is to compare radiation dosage and image quality between digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM). Methods and Materials: 303 patients (mean age 52.1 years) who studied DBT and FFDM were retrospectively reviewed. Radiation dosage data were obtained by radiation dosage scoring and monitoring program: Radimetrics (Bayer HealthCare, Whippany, NJ). Entrance dose and mean glandular doses in each breast were obtained in both imaging modalities. To compare the image quality of DBT with two-dimensional synthesized mammogram (2DSM) and FFDM, 5-point scoring of lesion clarity was assessed and the better modality between the two was selected. Interobserver performance was compared with kappa values and diagnostic accuracy was compared using McNemar test. The parameters of radiation dosages (entrance dose, mean glandular dose) and image quality were compared between two modalities by using paired t-test and Wilcoxon rank sum test. Results: For entrance dose and mean glandular doses for each breasts, DBT had lower values compared with FFDM (p-value < 0.0001). Diagnostic accuracy did not have statistical difference, but lesion clarity score was higher in DBT with 2DSM and DBT was chosen as a better modality compared with FFDM. Conclusion: DBT showed lower radiation entrance dose and also lower mean glandular doses to both breasts compared with FFDM. Also, DBT with 2DSM had better image quality than FFDM with similar diagnostic accuracy, suggesting that DBT may have a potential to be performed as an alternative to FFDM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radiation%20dose" title="radiation dose">radiation dose</a>, <a href="https://publications.waset.org/abstracts/search?q=DBT" title=" DBT"> DBT</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20mammography" title=" digital mammography"> digital mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20quality" title=" image quality"> image quality</a> </p> <a href="https://publications.waset.org/abstracts/79784/comparison-of-radiation-dosage-and-image-quality-digital-breast-tomosynthesis-vs-full-field-digital-mammography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79784.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Clinical Outcomes For Patients Diagnosed With DCIS Through The Breast Screening Programme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aisling%20Eves">Aisling Eves</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Pieri"> Andrew Pieri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ross%20McLean"> Ross McLean</a>, <a href="https://publications.waset.org/abstracts/search?q=Nerys%20Forester"> Nerys Forester</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: DCIS accounts for 20% of malignancies diagnosed by the breast screening programme and is primarily managed by surgical excision. There is variable guidance on defining excision margins, and adjuvant treatments vary widely. This study aimed to investigate the clinical outcomes for patients following surgical excision of small volume DCIS. Methods: This single-centreretrospective cohort study of 101 consecutive breast screened patients diagnosed with DCIS who underwent surgical excision. All patients diagnosed with DCIS had radiological abnormalities <15mm. Clinical, radiological, and histological data were collected from patients who had been diagnosed within a 5 year period, and ASCO guidelines for margin involvement of <2mm was used to guide the need for re-excision. Outcomes included re-excision rates, radiotherapy usage, and the presence of invasive cancer. Results: Breast conservation surgery was performed in 94.1% (n=95). Following surgical excision, 74(73.27%)patients had complete DCIS excision (>2mm margin), 4(4.0%) had margins 1-2mm, and 17(16.84%)had margins <1mm. The median size of DCIS in the specimen sample was 4mm. In 86% of patients with involved margins (n=18), the mammogram underestimated the DCIS size by a median of 12.5mm (range: 1-42mm). Of the patients with involved margins, 11(10.9%)had a re-excision, and 6 of these (50%) required two re-excisions to completely excise the DCIS. Post-operative radiotherapy was provided to 53(52.48%)patients. Four (3.97%) patients were found to have invasive ductal carcinoma on surgical excision, which was not present on core biopsy – all had high-grade DCIS. Recurrence of DCIS was seen in the same site during follow-up in 1 patient (1%), 1 year after their first DCIS diagnosis. Conclusion: Breast conservation surgery is safe in patients with DCIS, with low rates of re-excision, recurrence, and upstaging to invasive cancer. Furthermore, the median size of DCIS found in the specimens of patients who had DCIS fully removed in surgery was low, suggesting it may be possible that total removal through VAE was possible for these patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=surgical%20excision" title="surgical excision">surgical excision</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20conservation%20surgery" title=" breast conservation surgery"> breast conservation surgery</a>, <a href="https://publications.waset.org/abstracts/search?q=DCIS" title=" DCIS"> DCIS</a>, <a href="https://publications.waset.org/abstracts/search?q=Re-excision" title=" Re-excision"> Re-excision</a>, <a href="https://publications.waset.org/abstracts/search?q=radiotherapy" title=" radiotherapy"> radiotherapy</a>, <a href="https://publications.waset.org/abstracts/search?q=invasive%20cancer" title=" invasive cancer"> invasive cancer</a> </p> <a href="https://publications.waset.org/abstracts/146028/clinical-outcomes-for-patients-diagnosed-with-dcis-through-the-breast-screening-programme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146028.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">133</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Correlation of Leptin with Clinico-Pathological Features of Breast Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saad%20Al-Shibli">Saad Al-Shibli</a>, <a href="https://publications.waset.org/abstracts/search?q=Nasser%20Amjad"> Nasser Amjad</a>, <a href="https://publications.waset.org/abstracts/search?q=Muna%20Al%20Kubaisi"> Muna Al Kubaisi</a>, <a href="https://publications.waset.org/abstracts/search?q=Norra%20Harun"> Norra Harun</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaikh%20Mizan"> Shaikh Mizan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Leptin is a multifunctional hormone produced mainly by adipocyte. Leptin and its receptor have long been found associated with breast cancer. The main aim of this study is to investigate the correlation between Leptin/Leptin receptor and the clinicopathological features of breast cancer. Blood samples for ELISA, tissue samples from tumors and adjacent breast tissue were taken from 51 women with breast cancer with a control group of 40 women with a negative mammogram. Leptin and Leptin receptor in the tissues were estimated by immunohistochemistry (IHC). They were localized at the subcellular level by immunocytochemistry using transmission electron microscopy (TEM). Our results showed significant difference in serum leptin level between control and the patient group, but no difference between pre and post-operative serum leptin levels in the patient group. By IHC, we found that the majority of the breast cancer cells studied, stained positively for leptin and leptin receptors with co-expression of leptin and its receptors. No significant correlation was found between leptin/leptin receptors expression with the race, menopausal status, lymph node metastasis, estrogen receptor expression, progesterone receptor expression, HER2 expression and tumor size. Majority of the patients with distant metastasis were associated with high leptin and leptin receptor expression. TEM views both Leptin and Leptin receptor were found highly concentrated within and around the nucleus of the cancer breast cells, indicating nucleus is their principal seat of actions while the adjacent breast epithelial cells showed that leptin gold particles are scattered all over the cell with much less than that of the cancerous cells. However, presence of high concentration of leptin does not necessarily prove its over-expression, because it could be internalized from outside by leptin receptor in the cells. In contrast, leptin receptor is definitely over-expressed in the ductal breast cancer cells. We conclude that reducing leptin levels, blocking its downstream tissue specific signal transduction, and/or blocking the upstream leptin receptor pathway might help in prevention and therapy of breast cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=expression" title=" expression"> expression</a>, <a href="https://publications.waset.org/abstracts/search?q=leptin" title=" leptin"> leptin</a>, <a href="https://publications.waset.org/abstracts/search?q=leptin%20receptors" title=" leptin receptors"> leptin receptors</a> </p> <a href="https://publications.waset.org/abstracts/99477/correlation-of-leptin-with-clinico-pathological-features-of-breast-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99477.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Effects of the Affordable Care Act On Preventive Care Disparities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cagdas%20Agirdas">Cagdas Agirdas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The Affordable Care Act (ACA) requires non-grandfathered private insurance plans, starting with plan years on or after September 23rd, 2010, to provide certain preventive care services without any cost sharing in the form of deductibles, copayments or co-insurance. This requirement may affect racial and ethnic disparities in preventive care as it provides the largest copay reduction in preventive care. Objectives: We ask whether the ACA’s free preventive care benefits are associated with a reduction in racial and ethnic disparities in the utilization of four preventive services: cholesterol screenings, colonoscopies, mammograms, and pap smears. Methods: We use a data set of over 6,000 individuals from the 2009, 2010, and 2013 Medical Expenditure Panel Surveys (MEPS). We restrict our data set only to individuals who are old enough to be eligible for each preventive service. Our difference-in-differences logistic regression model classifies privately-insured Hispanics, African Americans, and Asians as the treatment groups and 2013 as the after-policy year. Our control group consists of non-Hispanic whites on Medicaid as this program already covered preventive care services for free or at a low cost before the ACA. Results: After controlling for income, education, marital status, preferred interview language, self-reported health status, employment, having a usual source of care, age and gender, we find that the ACA is associated with increases in the probability of the median, privately-insured Hispanic person to get a colonoscopy by 3.6% and a mammogram by 3.1%, compared to a non-Hispanic white person on Medicaid. Similarly, we find that the median, privately-insured African American person’s probability of receiving these two preventive services improved by 2.3% and 2.4% compared to a non-Hispanic white person on Medicaid. We do not find any significant improvements for any racial or ethnic group for cholesterol screenings or pap smears. Furthermore, our results do not indicate any significant changes for Asians compared to non-Hispanic whites in utilizing the four preventive services. These reductions in racial/ethnic disparities are robust to reconfigurations of time periods, previous diagnosis, and residential status. Conclusions: Early effects of the ACA’s provision of free preventive care are significant for Hispanics and African Americans. Further research is needed for the later years as more individuals became aware of these benefits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=preventive%20care" title="preventive care">preventive care</a>, <a href="https://publications.waset.org/abstracts/search?q=Affordable%20Care%20Act" title=" Affordable Care Act"> Affordable Care Act</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20sharing" title=" cost sharing"> cost sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=racial%20disparities" title=" racial disparities"> racial disparities</a> </p> <a href="https://publications.waset.org/abstracts/122759/effects-of-the-affordable-care-act-on-preventive-care-disparities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122759.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> The Anesthesia Considerations in Robotic Mastectomies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amrit%20Vasdev">Amrit Vasdev</a>, <a href="https://publications.waset.org/abstracts/search?q=Edwin%20Rho"> Edwin Rho</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurinder%20Vasdev"> Gurinder Vasdev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Robotic surgery has enabled a new spectrum of minimally invasive breast reconstruction by improving visualization, surgeon posturing, and improved patient outcomes.1 The DaVinci robot system can be utilized in nipple sparing mastectomies and reconstructions. The process involves the insufflation of the subglandular space and a dissection of the mammary gland with a combination of cautery and blunt dissection. This case outlines a 35-year-old woman who has a long-standing family history of breast cancer and a diagnosis of a deleterious BRCA2 genetic mutation. She has decided to proceed with bilateral nipple sparing mastectomies with implants. Her perioperative mammogram and MRI were negative for masses, however, her left internal mammary lymph node was enlarged. She has taken oral contraceptive pills for 3-5 years and denies DES exposure, radiation therapy, human replacement therapy, or prior breast surgery. She does not smoke and rarely consumes alcohol. During the procedure, the patient received a standardized anesthetic for out-patient surgery of propofol infusion, succinylcholine, sevoflurane, and fentanyl. Aprepitant was given as an antiemetic and preoperative Tylenol and gabapentin for pain management. Concerns for the patient during the procedure included CO2 insufflation into the subcutaneous space. With CO2 insufflation, there is a potential for rapid uptake leading to severe acidosis, embolism, and subcutaneous emphysema.2To mitigate this, it is important to hyperventilate the patient and reduce both the insufflation pressure and the CO2 flow rate to the minimal acceptable by the surgeon. For intraoperative monitoring during this 6-9 hour long procedure, it has been suggested to utilize an Arterial-Line for end-tidal CO2 monitoring. However, in this case, it was not necessary as the patient had excellent cardiovascular reserve, and end-tidal CO2 was within normal limits for the duration of the procedure. A BIS monitor was also utilized to reduce anesthesia burden and to facilitate a prompt discharge from the PACU. Minimal Invasive Robotic Surgery will continue to evolve, and anesthesiologists need to be prepared for the new challenges ahead. Based on our limit number of patients, robotic mastectomy appears to be a safe alternative to open surgery with the promise of clearer tissue demarcation and better cosmetic results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anesthesia" title="anesthesia">anesthesia</a>, <a href="https://publications.waset.org/abstracts/search?q=mastectomies" title=" mastectomies"> mastectomies</a>, <a href="https://publications.waset.org/abstracts/search?q=robotic" title=" robotic"> robotic</a>, <a href="https://publications.waset.org/abstracts/search?q=hypercarbia" title=" hypercarbia"> hypercarbia</a> </p> <a href="https://publications.waset.org/abstracts/164688/the-anesthesia-considerations-in-robotic-mastectomies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164688.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">112</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Call-Back Laterality and Bilaterality: Possible Screening Mammography Quality Metrics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samson%20Munn">Samson Munn</a>, <a href="https://publications.waset.org/abstracts/search?q=Virginia%20H.%20Kim"> Virginia H. Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Huija%20Chen"> Huija Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Sean%20Maldonado"> Sean Maldonado</a>, <a href="https://publications.waset.org/abstracts/search?q=Michelle%20Kim"> Michelle Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Koscheski"> Paul Koscheski</a>, <a href="https://publications.waset.org/abstracts/search?q=Babak%20N.%20Kalantari"> Babak N. Kalantari</a>, <a href="https://publications.waset.org/abstracts/search?q=Gregory%20Eckel"> Gregory Eckel</a>, <a href="https://publications.waset.org/abstracts/search?q=Albert%20Lee"> Albert Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In terms of screening mammography quality, neither the portion of reports that advise call-back imaging that should be bilateral versus unilateral nor how much the unilateral call-backs may appropriately diverge from 50–50 (left versus right) is known. Many factors may affect detection laterality: display arrangement, reflections preferentially striking one display location, hanging protocols, seating positions with respect to others and displays, visual field cuts, health, etc. The call-back bilateral fraction may reflect radiologist experience (not in our data) or confidence level. Thus, laterality and bilaterality of call-backs advised in screening mammography reports could be worthy quality metrics. Here, laterality data did not reveal a concern until drilling down to individuals. Bilateral screening mammogram report recommendations by five breast imaging, attending radiologists at Harbor-UCLA Medical Center (Torrance, California) 9/1/15--8/31/16 and 9/1/16--8/31/17 were retrospectively reviewed. Recommended call-backs for bilateral versus unilateral, and for left versus right, findings were counted. Chi-square (χ²) statistic was applied. Year 1: of 2,665 bilateral screening mammograms, reports of 556 (20.9%) recommended call-back, of which 99 (17.8% of the 556) were for bilateral findings. Of the 457 unilateral recommendations, 222 (48.6%) regarded the left breast. Year 2: of 2,106 bilateral screening mammograms, reports of 439 (20.8%) recommended call-back, of which 65 (14.8% of the 439) were for bilateral findings. Of the 374 unilateral recommendations, 182 (48.7%) regarded the left breast. Individual ranges of call-backs that were bilateral were 13.2–23.3%, 10.2–22.5%, and 13.6–17.9%, by year(s) 1, 2, and 1+2, respectively; these ranges were unrelated to experience level; the two-year mean was 15.8% (SD=1.9%). The lowest χ² p value of the group's sidedness disparities years 1, 2, and 1+2 was > 0.4. Regarding four individual radiologists, the lowest p value was 0.42. However, the fifth radiologist disfavored the left, with p values of 0.21, 0.19, and 0.07, respectively; that radiologist had the greatest number of years of experience. There was a concerning, 93% likelihood that bias against left breast findings evidenced by one of our radiologists was not random. Notably, very soon after the period under review, he retired, presented with leukemia, and died. We call for research to be done, particularly by large departments with many radiologists, of two possible, new, quality metrics in screening mammography: laterality and bilaterality. (Images, patient outcomes, report validity, and radiologist psychological confidence levels were not assessed. No intervention nor subsequent data collection was conducted. This uncomplicated collection of data and simple appraisal were not designed, nor had there been any intention to develop or contribute, to generalizable knowledge (per U.S. DHHS 45 CFR, part 46)). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mammography" title="mammography">mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=screening%20mammography" title=" screening mammography"> screening mammography</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20metrics" title=" quality metrics"> quality metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=laterality" title=" laterality"> laterality</a> </p> <a href="https://publications.waset.org/abstracts/133741/call-back-laterality-and-bilaterality-possible-screening-mammography-quality-metrics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133741.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul 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