Automatic Classification of Lung Diseases from CT Images

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Diseases from CT Images</title> <meta name="description" content="Automatic Classification of Lung Diseases from CT Images"> <meta name="keywords" content="CT scans, COVID-19, deep learning, image processing, pneumonia, lung disease."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Automatic Classification of Lung Diseases from CT Images"> <meta name="citation_author" content="Abobaker Mohammed Qasem Farhan"> <meta name="citation_author" content="Shangming Yang"> <meta name="citation_author" content="Mohammed Al-Nehari"> <meta name="citation_publication_date" content="2023/04/13"> <meta name="citation_journal_title" content="International Journal of Health and Medical Engineering"> <meta name="citation_volume" content="17"> <meta name="citation_issue" content="4"> <meta name="citation_firstpage" content="87"> <meta name="citation_lastpage" content="91"> 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href="https://publications.waset.org/search?q=Shangming%20Yang"> Shangming Yang</a>, <a href="https://publications.waset.org/search?q=Mohammed%20Al-Nehari"> Mohammed Al-Nehari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life due to the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or COVID-19 induced pneumonia. The early prediction and classification of such lung diseases help reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans are pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publicly available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.</p> <iframe src="https://publications.waset.org/10013059.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=CT%20scans" title="CT scans">CT scans</a>, <a href="https://publications.waset.org/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/search?q=pneumonia" title=" pneumonia"> pneumonia</a>, <a href="https://publications.waset.org/search?q=lung%20disease." title=" lung disease."> lung disease.</a> </p> <a href="https://publications.waset.org/10013059/automatic-classification-of-lung-diseases-from-ct-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013059/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013059/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013059/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013059/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013059/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013059/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013059/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013059/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013059/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013059/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013059.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">610</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] Elibol, E. 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Automatic Classification of Lung Diseases from CT Images