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The approach combines a modified VGG19 model for classifying DBT images with a YOLOv5-CBAM model for tumor detection and discrimination between benign and malignant lesions. VGG19 is enhanced by adding batch normalization and pooling layers, while CBAM is integrated into YOLOv5 after feature fusion. Experiments on 22,032 DBT examinations from 5,060 patients show improved test accuracy, reduced training loss, and higher training accuracy compared with previous models.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/breast-cancer-detection-and-classification-with-digital-breast-tomosynthesis-a-two-stage-deep-learning-approach/329908/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/breast-cancer-detection-and-classification-with-digital-breast-tomosynthesis-a-two-stage-deep-learning-approach/329908.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of the proposed system?","Question",{"text":113,"@type":114},"To build a two-stage computer-assisted diagnosis system that classifies digital breast tomosynthesis images and detects tumors as benign or cancerous using deep learning models.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are the models modified and combined in this approach?",{"text":118,"@type":114},"A modified VGG19 adds extra batch normalization and pooling layers for DBT image classification, while YOLOv5-CBAM incorporates the CBAM module into YOLOv5 after each feature fusion for lesion detection.",{"name":120,"@type":111,"acceptedAnswer":121},"What dataset size and experimental setup are used?",{"text":122,"@type":114},"The study uses a benchmark dataset containing 22,032 DBT examinations from 5,060 patients to evaluate performance.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},329908,1790232771,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","Copyright© Author(s) -[Available online at dirjournal.org.](Available online at dirjournal.org. ORIGINAL)[ ORIGINAL](Available online at dirjournal.org. ORIGINAL) ARTICLE  \nContent of this journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.  \nBreast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach  \nYazeed Alashban  \nKing Saud University, College of Applied Medical Sciences, Department of Radiological Sciences, Riyadh, Saudi Arabia  \nCorresponding author: Yazeed Alashban  \n[E-mail:](E-mail: Yalashban@ksu.edu.sa)[ Yalashban@ksu.edu.sa](E-mail: Yalashban@ksu.edu.sa)  \nReceived 02 July 2024; revision requested 05 September 2024; accepted 12 October 2024.  \nEpub: 09.12.2024  \nPublication date: 28.04.2025  \nDOI: 10.4274/dir.2024.242923  \nPURPOSE  \nThe purpose of this study was to propose a new computer-assisted two-staged diagnosis system that combines a modified deep learning (DL) architecture (VGG19) for the classification of digital breast tomosynthesis (DBT) images with the detection of tumors as benign or cancerous using the You Only Look Once version 5 (YOLOv5) model combined with the convolutional block attention module (CBAM) (known as YOLOv5-CBAM) .  \nMETHODS  \nIn the modified version of VGG19, eight additional layers were integrated, comprising four batch normalization layers and four additional pooling layers (two max pooling and two average pooling) . The CBAM was incorporated into the YOLOv5 model structure after each feature fusion. The experiment was carried out using a sizable benchmark dataset of breast tomography images. A total of 22,032 DBT examinations from 5,060 patients were included in the data.  \nRESULTS  \nTest accuracy, training loss, and training accuracy showed better performance with our proposed architecture than with previous models. Hence, the modified VGG19 classified DBT images more accurately than previously possible using pre-trained model-based architectures. Furthermore, a YOLOv5-based CBAM precisely discriminated between benign lesions and those that were malignant.  \nCONCLUSION  \nDBT images can be classified using modified VGG19 with accuracy greater than the previously available pre-trained models-based architectures. Furthermore, a YOLOv5-based CBAM can precisely distinguish between benign and cancerous lesions.  \nCLINICAL SIGNIFICANCE  \nThe proposed two-tier DL algorithm, combining a modified VGG19 model for image classification and YOLOv5-CBAM for lesion detection, can improve the accuracy, efficiency, and reliability of breast cancer screening and diagnosis through innovative artificial intelligence-driven methodologies.  \nKEYWORDS  \nBreast imaging, artificial intelligence, mammography, breast cancer  \nB  \nreast cancer (BC) is one of the main causes of mortality in women and a major global health concern.1 According to data from the World Health Organization, in 2022, 665,684 women worldwide lost their lives due to malignancy in the breast, accounting for 2.3  \nmillion new cases of the disease. BC is the most common cancer globally among women; between 2015 and 2021, 7.8 million women were diagnosed with the disease.2 In the US, BC ranks as the second most common malignancy after lung cancer, according to the Surveillance, Epidemiology, and End Results Program.3 Globally, according to available data,3,4 one in eight women will contract BC. As a result, BC screening is one of the most significant and common  \nYou may cite this article as: Alashban Y. Breast cancer detection and classification with digital breast tomosynthesis: a two-stage deep learning approach. Diagn Interv Radiol. 2025;31(3):206-214 .  \nmedical imaging prerequisites, with over 39 million examinations carried out annually.5 Early identification and discovery are essential for therapy, rehabilitation, and a decrease in death rates.6 The prognosis and survival rate of cancer vary greatly depending on its stage. Cancer tre","cbCaiuebzdPcVj5b","https://ap.wps.com/l/cbCaiuebzdPcVj5b","pdf",2044464,"English","# PURPOSE\n# METHODS\n# RESULTS\n# CONCLUSION\n# CLINICAL SIGNIFICANCE\n# KEYWORDS","[{\"question\":\"What is the main goal of the proposed system?\",\"answer\":\"To build a two-stage computer-assisted diagnosis system that classifies digital breast tomosynthesis images and detects tumors as benign or cancerous using deep learning models.\"},{\"question\":\"How are the models modified and combined in this approach?\",\"answer\":\"A modified VGG19 adds extra batch normalization and pooling layers for DBT image classification, while YOLOv5-CBAM incorporates the CBAM module into YOLOv5 after each feature fusion for lesion detection.\"},{\"question\":\"What dataset size and experimental setup are used?\",\"answer\":\"The study uses a benchmark dataset containing 22,032 DBT examinations from 5,060 patients to evaluate performance.\"}]","Breast cancer detection and classification with digital breast tomosynthesis - a two-stage deep learning approach | PDF",1789986117,23]