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(PDF) Melanoma Detection via Structural and Textural Feature Fusion
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For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of" /> <meta name="twitter:image" content="https://0.academia-photos.com/263837202/115339392/104625007/s200_fakhreddine.ababsa.png" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition" /> <meta property="og:title" content="Fusion of structural and textural features for melanoma recognition" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of" /> <meta property="article:author" content="https://gadz.academia.edu/FakhreddineAbabsa" /> <meta name="description" content="Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of" /> <title>(PDF) Melanoma Detection via Structural and Textural Feature Fusion</title> <link rel="canonical" href="https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = 'a780cc88a085c47718c74cbee01ed38f9303fee5'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1733268219000); window.Aedu.timeDifference = new Date().getTime() - 1733268219000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperfor...","author":[{"@context":"https://schema.org","@type":"Person","name":"Fakhreddine Ababsa"}],"contributor":[],"dateCreated":"2023-12-16","dateModified":"2024-11-29","datePublished":"2018-01-01","headline":"Fusion of structural and textural features for melanoma recognition","image":"https://attachments.academia-assets.com/109098762/thumbnails/1.jpg","inLanguage":"en","keywords":["Cognitive Science","Computer Science","Artificial Intelligence","Medical Image Processing","Cancer","Support Vector Machines","Image fusion","Wavelet Transforms","Fusion","Feature Extraction","Image recognition","Support vector machine","Curvelet","Dermoscopy","Cancers","Electrical And Electronic Engineering","Textural Features","Local Binary Patterns","Structural Features"],"publication":"IET Computer Vision","publisher":{"@context":"https://schema.org","@type":"Organization","name":"Institution of Engineering and Technology (IET)"},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"gadz"}],"thumbnailUrl":"https://attachments.academia-assets.com/109098762/thumbnails/1.jpg","url":"https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition"}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/single_work_page/loswp-102fa537001ba4d8dcd921ad9bd56c474abc201906ea4843e7e7efe9dfbf561d.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-8d679e925718b5e8e4b18e9a4fab37f7eaa99e43386459376559080ac8f2856a.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-3cea6e0ad4715ed965c49bfb15dedfc632787b32ff6d8c3a474182b231146ab7.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/text_button-73590134e40cdb49f9abdc8e796cc00dc362693f3f0f6137d6cf9bb78c318ce7.css" /><link 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For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperfor...","publisher":"Institution of Engineering and Technology (IET)","ai_title_tag":"Melanoma Detection via Structural and Textural Feature Fusion","publication_date":"2018,,","publication_name":"IET Computer Vision"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Fusion of structural and textural features for melanoma recognition","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [263837202]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":109098762,"attachmentType":"pdf"}"><img alt="First page of “Fusion of structural and textural features for melanoma recognition”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/109098762/mini_magick20231217-1-yebge7.png?1702800193" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Fusion of structural and textural features for melanoma recognition</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="263837202" href="https://gadz.academia.edu/FakhreddineAbabsa"><img alt="Profile image of Fakhreddine Ababsa" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/263837202/115339392/104625007/s65_fakhreddine.ababsa.png" />Fakhreddine Ababsa</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2018, IET Computer Vision</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">7 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 111609800; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=111609800"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { const viewCountNumber = Number(viewCount); if (!viewCountNumber) { throw new Error('Failed to parse view count'); } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (viewCountBody) { viewCountBody.textContent = `${commaizedViewCount} views`; } else { throw new Error('Failed to find work views element'); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Melanoma is one the most increasing cancers since past decades. For accurate detection and classification, discriminative features are required to distinguish between benign and malignant cases. In this study, the authors introduce a fusion of structural and textural features from two descriptors. The structural features are extracted from wavelet and curvelet transforms, whereas the textural features are extracted from different variants of local binary pattern operator. The proposed method is implemented on 200 images from dermoscopy database including 160 non‐melanoma and 40 melanoma images, where a rigorous statistical analysis for the database is performed. Using support vector machine (SVM) classifier with random sampling cross‐validation method between the three cases of skin lesions given in the database, the validated results showed a very encouraging performance with a sensitivity of 78.93%, a specificity of 93.25% and an accuracy of 86.07%. The proposed approach outperfor...</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":109098762,"attachmentType":"pdf","workUrl":"https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":109098762,"attachmentType":"pdf","workUrl":"https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="109098762" data-landing_url="https://www.academia.edu/111609800/Fusion_of_structural_and_textural_features_for_melanoma_recognition" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="31905181" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/31905181/Automatic_Recognition_of_Melanoma_Using_Support_Vector_Machines_A_Study_Based_on_Wavelet_Curvelet_and_Color_Features">Automatic Recognition of Melanoma Using Support Vector Machines: A Study Based on Wavelet, Curvelet and Color Features</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="24918733" href="https://mansura.academia.edu/MohamedKhaled">Mohamed Khaled</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This paper proposes an automated non-invasive system for skin cancer (melanoma) detection based on Support Vector Machine classification. The proposed system uses a number of features extracted from the Wavelet or the Curvelet decomposition of the grayscale skin lesion images and color features obtained from the original color images. The dataset used include both digital images and Dermoscopy images for skin lesions that are either benign or malignant. The recognition accuracy obtained by the Support Vector Machine classifier used in this experiment is 87.7.1% for the Wavelet based features and 83.6. 6% for the Curvelet based ones. The proposed system also resulted in a sensitivity of 86.4 % for the case of Wavelet and 76.9% for the case of Curvelet. It also resulted in a specificity of 88.1% for the case of Wavelet and 85.4% for the case of Curvelet. The obtained sensitivity and specificity results are comparable to those obtained by Dermatologists.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Automatic Recognition of Melanoma Using Support Vector Machines: A Study Based on Wavelet, Curvelet and Color Features","attachmentId":52188389,"attachmentType":"pdf","work_url":"https://www.academia.edu/31905181/Automatic_Recognition_of_Melanoma_Using_Support_Vector_Machines_A_Study_Based_on_Wavelet_Curvelet_and_Color_Features","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/31905181/Automatic_Recognition_of_Melanoma_Using_Support_Vector_Machines_A_Study_Based_on_Wavelet_Curvelet_and_Color_Features"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="68563282" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/68563282/Automatic_Detection_of_Melanoma_Skin_Cancer_using_Texture_Analysis">Automatic Detection of Melanoma Skin Cancer using Texture Analysis</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="27975253" href="https://independent.academia.edu/AmrSharawi">Amr Sharawi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computer Applications, 2012</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Automatic Detection of Melanoma Skin Cancer using Texture Analysis","attachmentId":78995209,"attachmentType":"pdf","work_url":"https://www.academia.edu/68563282/Automatic_Detection_of_Melanoma_Skin_Cancer_using_Texture_Analysis","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/68563282/Automatic_Detection_of_Melanoma_Skin_Cancer_using_Texture_Analysis"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="79202898" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/79202898/Resolution_invariant_wavelet_features_of_melanoma_studied_by_SVM_classifiers">Resolution invariant wavelet features of melanoma studied by SVM classifiers</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="37957513" href="https://independent.academia.edu/GrzegorzSur%C3%B3wka">Grzegorz Surówka</a></div><p class="ds-related-work--metadata ds2-5-body-xs">PLOS ONE, 2019</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Resolution invariant wavelet features of melanoma studied by SVM classifiers","attachmentId":85997223,"attachmentType":"pdf","work_url":"https://www.academia.edu/79202898/Resolution_invariant_wavelet_features_of_melanoma_studied_by_SVM_classifiers","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/79202898/Resolution_invariant_wavelet_features_of_melanoma_studied_by_SVM_classifiers"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="78881791" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/78881791/Malignant_Melanoma_Detection_Based_on_Machine_Learning_Techniques_A_Survey_1">Malignant Melanoma Detection Based on Machine Learning Techniques : A Survey 1</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="1100417" href="https://aden-univ.academia.edu/munyaarasi">Munya Arasi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2016</p><p class="ds-related-work--abstract ds2-5-body-sm">Skin cancer is one of the most growing types and dangerous cancer in the world; the important of these cancers are malignant melanoma. The early diagnosis of malignant melanoma is a critical issue for dermatologists. In this paper, we present an overview of recent the state of the art in Computer-aided detection/diagnosis (CAD) systems in identifying and diagnosing malignant melanoma of dermoscopy images and describe its steps starting with image acquisition, preprocessing; and finishing with malignant melanoma classification of dermoscopic images. The comparative study shows that the most common methods for features extraction are the Discreet Wavelet Transform (DWT) and the method which combines both texture and color features resulting in output of very high accuracy. The methods for the classification:K-Nearest Neighbor, Artificial Neural Networks, and Support Vector Machines are very well in the range [%90 –% 97, 5].</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Malignant Melanoma Detection Based on Machine Learning Techniques : A Survey 1","attachmentId":85768080,"attachmentType":"pdf","work_url":"https://www.academia.edu/78881791/Malignant_Melanoma_Detection_Based_on_Machine_Learning_Techniques_A_Survey_1","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/78881791/Malignant_Melanoma_Detection_Based_on_Machine_Learning_Techniques_A_Survey_1"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="82190184" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/82190184/Melanoma_detection_using_color_and_texture_features_in_computer_vision_systems">Melanoma detection using color and texture features in computer vision systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="161893240" href="https://independent.academia.edu/EsterZumpano">Ester Zumpano</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Advances in Science, Technology and Engineering Systems Journal, 2019</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Melanoma detection using color and texture features in computer vision systems","attachmentId":87974925,"attachmentType":"pdf","work_url":"https://www.academia.edu/82190184/Melanoma_detection_using_color_and_texture_features_in_computer_vision_systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/82190184/Melanoma_detection_using_color_and_texture_features_in_computer_vision_systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="80047266" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/80047266/Hybrid_Feature_Fusion_and_Machine_Learning_Approaches_for_Melanoma_Skin_Cancer_Detection">Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="63910194" href="https://classics-rutgers.academia.edu/imandehzangi">iman dehzangi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract a...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection","attachmentId":86558910,"attachmentType":"pdf","work_url":"https://www.academia.edu/80047266/Hybrid_Feature_Fusion_and_Machine_Learning_Approaches_for_Melanoma_Skin_Cancer_Detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/80047266/Hybrid_Feature_Fusion_and_Machine_Learning_Approaches_for_Melanoma_Skin_Cancer_Detection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="113280440" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/113280440/Computer_Vision_Based_Skin_Cancer_Classification_by_Using_Texture_Features">Computer Vision Based Skin Cancer Classification by Using Texture Features</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="290868774" href="https://independent.academia.edu/AqibAli227">Aqib Ali</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &amp; Computational Sciences and Engineering, 8th ed. - MOL2NET: FROM MOLECULES TO NETWORKS</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Computer Vision Based Skin Cancer Classification by Using Texture Features","attachmentId":110280688,"attachmentType":"pdf","work_url":"https://www.academia.edu/113280440/Computer_Vision_Based_Skin_Cancer_Classification_by_Using_Texture_Features","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/113280440/Computer_Vision_Based_Skin_Cancer_Classification_by_Using_Texture_Features"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="12213267" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/12213267/Two_systems_for_the_detection_of_melanomas_in_dermoscopy_images_using_texture_and_color_features">Two systems for the detection of melanomas in dermoscopy images using texture and color features</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="30689266" href="https://unigranrio.academia.edu/TMendon%C3%A7a">T. Mendonça</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Two systems for the detection of melanomas in dermoscopy images using texture and color features","attachmentId":46299502,"attachmentType":"pdf","work_url":"https://www.academia.edu/12213267/Two_systems_for_the_detection_of_melanomas_in_dermoscopy_images_using_texture_and_color_features","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/12213267/Two_systems_for_the_detection_of_melanomas_in_dermoscopy_images_using_texture_and_color_features"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="43174662" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/43174662/Classification_of_Melanoma_and_Nevus_in_Digital_Images_for_Diagnosis_of_Skin_Cancer">Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="656912" href="https://ijera.academia.edu/ijera">IJERA Journal</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Melanoma is considered a fatal type of skin cancer. However, it is sometimes hard to distinguish it from Nevus due to their identical visual appearance and symptoms. The mortality rate because of this disease is higher than all other skin related consolidated malignancies. The number of cases is growing amongst young people but if it is diagnosed at its earlier stage then the survival rates become very high. The cost and time required for the doctors to diagnose all patients for Melanoma are very high. In this research work, we propose an intelligent system to detect and distinguish Melanoma from Nevus by using state of the art image processing techniques. At first, Gaussian Filter is used for removing noise from the skin lesion of the acquired images followed by the use of improved K-mean clustering to segment out the lesion. A distinctive hybrid super feature vector is formed by the extraction of textural and color features from the lesion. Support Vector Machine (SVM) is utilized for the classification of skin cancer into melanoma and nevus. Our aim is to test the effectiveness of the proposed segmentation technique, extract the most suitable features and compare the classification results with the other techniques present in the literature. The proposed methodology is tested on DERMIS dataset having a total number of 397 skin cancer images where 146 are melanoma and 251 are nevus skinlesions. Our proposed methodology archives encouraging resultshaving 96% accuracy.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer","attachmentId":63438439,"attachmentType":"pdf","work_url":"https://www.academia.edu/43174662/Classification_of_Melanoma_and_Nevus_in_Digital_Images_for_Diagnosis_of_Skin_Cancer","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/43174662/Classification_of_Melanoma_and_Nevus_in_Digital_Images_for_Diagnosis_of_Skin_Cancer"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="12215807" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/12215807/Wavelet_and_Curvelet_Analysis_for_Automatic_Identification_of_Melanoma_Based_on_Neural_Network_Classification">Wavelet and Curvelet Analysis for Automatic Identification of Melanoma Based on Neural Network Classification</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="30700012" href="https://uts.academia.edu/MohamedKhaledAbuMahmoud">Mohamed Khaled Abu Mahmoud</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Wavelet and Curvelet Analysis for Automatic Identification of Melanoma Based on Neural Network Classification","attachmentId":37493556,"attachmentType":"pdf","work_url":"https://www.academia.edu/12215807/Wavelet_and_Curvelet_Analysis_for_Automatic_Identification_of_Melanoma_Based_on_Neural_Network_Classification","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/12215807/Wavelet_and_Curvelet_Analysis_for_Automatic_Identification_of_Melanoma_Based_on_Neural_Network_Classification"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":109098762,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":109098762,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_109098762" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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