{"id":3407,"date":"2024-08-27T19:26:24","date_gmt":"2024-08-27T19:26:24","guid":{"rendered":"http:\/\/journal.ziu-university.net\/?p=3407"},"modified":"2024-08-27T19:26:25","modified_gmt":"2024-08-27T19:26:25","slug":"a-hybrid-deep-learning-model-for-breast-cancer-mammographic-image-classification-based-on-transfer-learning-and-an-attention-module","status":"publish","type":"post","link":"https:\/\/journal.ziu.edu.sy\/?p=3407","title":{"rendered":"A hybrid deep learning model for breast cancer Mammographic Image Classification based on transfer learning and an attention module"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\" dir=\"ltr\"><strong>Abstract<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" dir=\"ltr\">Breast cancer is one of the primary causes of death among women. Early detection of breast cancer allows for the receipt of appropriate treatment, thus increasing the possibility of survival. In this paper, we proposed a hybrid deep learning model using a pre-trained VGG16 model with a self-attention mechanism for breast cancer detection. We extract features from the binary class (benign, malignant) dataset of the mammographic image analysis society (MIAS) using pre-trained deep convolutional neural network (CNN) architectures like Xception, MobileNet, DenseNet, and VGG-16. So the results illustrated that the best model is VGG16 with a self-attention module, which achieved an accuracy of&nbsp; 98.77%.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" dir=\"ltr\"><strong>Keywords: <\/strong>Breast cancer, VGG16, MIAS,&nbsp; Mammography, Classification<strong>.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" dir=\"ltr\"><strong>BY : <\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" dir=\"ltr\">Tawfik Ezat Mousa\u00b9, Mohamed S. Geoda\u00b2<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" dir=\"ltr\">\u00b9\u00b2 Departement of Computer Technologies,<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" dir=\"ltr\">Higher Institute Of Science and Technology, Tobruk, Libya.<\/p>\n\r\n\t\t<div class=\"review_wrap\">\r\n\t\t\t<div id=\"review-box\" class=\"review-box review-bottom review-stars\">\r\n\t\t\t\t<div class=\"review-summary\">\r\n\t\t\t\t\t<div class=\"review-final-score\">\r\n\t\t\t\t\t\t<span title=\"\" class=\"post-large-rate stars-large\"><span style=\"width:0%\"><\/span><\/span>\r\n\t\t\t\t\t\t<h4><\/h4>\r\n\t\t\t\t\t<\/div>\r\n\t\t\t\t\r\n\t\t\t\t<div class=\"review-short-summary\"><a href=\"https:\/\/drive.google.com\/file\/d\/1rI_S0OOd6diYP2b7hN7Hz_L6IcSdbDmt\/view?usp=drive_link\" class=\"taq-button taq-medium taq-square taq-flat\" style=\"background-color:#a0ce4e\" target=\"_blank\"><i class=\"fa fa-download\"><\/i><span class=\"button-text\">\u062a\u062d\u0645\u064a\u0644<\/span><\/a>\r\n\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t\t\r\n\t\t\t<div class=\"user-rate-wrap\">\r\n\t\t\t\t<span class=\"user-rating-text\">\r\n\t\t\t\t\t<strong>\u062a\u0642\u064a\u064a\u0645 \u0627\u0644\u0645\u0633\u062a\u062e\u062f\u0645\u0648\u0646: <\/strong>\r\n\t\t\t\t\t<span class=\"taq-score\"><\/span>\r\n\t\t\t\t\t<small>\u0643\u0646 \u0623\u0648\u0644 \u0627\u0644\u0645\u0635\u0648\u062a\u0648\u0646 !<\/small>\r\n\t\t\t\t<\/span>\r\n\r\n\t\t\t\t<div data-rate=\"0\" data-id=\"3407\" class=\"user-rate taq-user-rate-active\">\r\n\t\t\t\t\t<span class=\"user-rate-image post-large-rate stars-large\">\r\n\t\t\t\t\t\t<span style=\"width:0%\"><\/span>\r\n\t\t\t\t\t<\/span>\r\n\t\t\t\t<\/div>\r\n\r\n\t\t\t\t<div class=\"taq-clear\"><\/div>\r\n\r\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Abstract Breast cancer is one of the primary causes of death among women. Early detection of breast cancer allows for the receipt of appropriate treatment, thus increasing the possibility of survival. In this paper, we proposed a hybrid deep learning model using a pre-trained VGG16 model with a self-attention mechanism for breast cancer detection. We &hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[123],"tags":[],"class_list":["post-3407","post","type-post","status-publish","format-standard","","category----1"],"_links":{"self":[{"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=\/wp\/v2\/posts\/3407","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3407"}],"version-history":[{"count":1,"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=\/wp\/v2\/posts\/3407\/revisions"}],"predecessor-version":[{"id":3408,"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=\/wp\/v2\/posts\/3407\/revisions\/3408"}],"wp:attachment":[{"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3407"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3407"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journal.ziu.edu.sy\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3407"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}