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The approach employs a ResNet-50 convolutional neural network and transfer learning from ImageNet to train on the INbreast dataset. Results report 93% classification accuracy, indicating improved early detection support for radiologists and potential reduction of screening errors, thereby saving medical time and resources.",{"@graph":69,"@context":122},[70,84,105],{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/efficient-breast-cancer-diagnosis-from-complex-mammographic-images-using-deep-convolutional-neural-network-research-article/384492/",{"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/efficient-breast-cancer-diagnosis-from-complex-mammographic-images-using-deep-convolutional-neural-network-research-article/384492.png","ImageObject",300,407,{"name":92,"@type":93},"moss~","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in breast cancer diagnosis?","Question",{"text":112,"@type":113},"It targets the difficulty of early-stage detection in mammography by using automated analysis to improve accuracy and support radiologists.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed method classify mammogram images?",{"text":117,"@type":113},"It uses a ResNet-50 convolutional neural network with transfer learning to categorize the INbreast dataset images as benign or malignant.",{"name":119,"@type":110,"acceptedAnswer":120},"What performance does the framework achieve?",{"text":121,"@type":113},"The reported classification accuracy is 93%, stated to surpass other models trained on the same dataset.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},384492,1790316616,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"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},962090895452,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Hindawi  \nComputational Intelligence and Neuroscience Volume 2023, Article ID 7717712, 11 pages [https://doi.org/10.1155/2023/7717712](https://doi.org/10.1155/2023/7717712)  \nResearch Article  \nEfficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network  \nHameedur Rahman , 1 Tanvir Fatima Naik Bukht,2 Rozilawati Ahmad ,3  \nAhmad Almadhor,4 and Abdul Rehman Javed 5,6  \n1 Department of Computer Games Development, Faculty of Computing and AI, Air University, E9, Islamabad, Pakistan 2 Department of Computer Science, Faculty of Computing and AI, Air University, E9, Islamabad, Pakistan  \n3 Diagnostic Imaging and Radiotherapy Program, Terapeutic and Investigation Studies, Faculty of Health Sciences,  \nUniversity Kebangsaan Malaysia, Kuala Lumpur, Malaysia  \n4 Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University,  \nSakaka 72388, Saudi Arabia  \n5 Department of Cyber Security, Air University, Islamabad, Pakistan  \n6 Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon  \nCorrespondence should be addressed to Hameedur Rahman; [hameed.rahman@mail.au.edu.pk](hameed.rahman@mail.au.edu.pk), Rozilawati Ahmad;  \n[rozie.ahmad@ukm.edu.my](rozie.ahmad@ukm.edu.my), and Abdul Rehman Javed; [abdulrehman.cs@au.edu.pk](abdulrehman.cs@au.edu.pk)  \nReceived 17 December 2022; Revised 15 February 2023; Accepted 23 February 2023; Published 2 March 2023  \nAcademic Editor: Najib Ben Aoun  \nCopyright © 2023 Hameedur Rahman et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nMedical image analysis places a signifcant focus on breast cancer, which poses a signifcant threat to women’s health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can signifcantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classifcation processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difcult. Te deep convolutional neural network has demonstrated exceptional results and is considered a highly efective tool in the feld. Tis study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. Te results revealed that the proposed framework achieved an outstanding classifcation accuracy of 93%, surpassing other models trained on the same dataset. Tis novel approach facilitates early diagnosis and classifcation of malignant and benign breast cancer, potentially saving lives and resources. Tese outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.  \n1. Introduction  \nTe predominant cause of cancer-related deaths among women globally is breast cancer [1, 2] . According to a report by the World Health Organization’s (WHO) cancer research institute, the International Agency for Research on Cancer, in 2018 globally, 17.1 million breast cancer cases were reported. Te number of cases is predicted to increase to double the amount by 2025 [3] . Breast cancer is a highly  \ninvasive tumor that primarily afects women [4] . Te high death rate among women makes it the second deadliest malignancy after lung cancer [5, 6] . A study by the National Institute of Cancer in China found 1.67 million breast cancer","cbCainqSK9sawe7e","https://ap.wps.com/l/cbCainqSK9sawe7e","pdf",841356,11,"English","# Introduction\n## Breast cancer burden and diagnosis needs\n## Tumor types and BI-RADS-based assessment\n## Imaging modalities and CAD motivation\n## Deep learning for automated CAD systems","[{\"question\":\"What problem does the study address in breast cancer diagnosis?\",\"answer\":\"It targets the difficulty of early-stage detection in mammography by using automated analysis to improve accuracy and support radiologists.\"},{\"question\":\"How does the proposed method classify mammogram images?\",\"answer\":\"It uses a ResNet-50 convolutional neural network with transfer learning to categorize the INbreast dataset images as benign or malignant.\"},{\"question\":\"What performance does the framework achieve?\",\"answer\":\"The reported classification accuracy is 93%, stated to surpass other models trained on the same dataset.\"}]","Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network - Research Article | PDF",1790263683,28]