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The paper investigates early detection using mammogram images and deep-learning models, leveraging four public databases containing 986 mammograms per class (normal, benign, malignant). Three CNN base classifiers—VGG-11, Inception v3, and ResNet50—are combined using a fuzzy ranking built from a modified Gompertz function and adaptive score integration. The fuzzy ensemble outperforms transfer learning and alternative ensemble methods, reaching 98.986% accuracy.",{"@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":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/deep-transfer-learning-with-fuzzy-ensemble-approach-for-the-early-detection-of-breast-cancer/338152/",{"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/deep-transfer-learning-with-fuzzy-ensemble-approach-for-the-early-detection-of-breast-cancer/338152.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","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 problem does the paper address?","Question",{"text":113,"@type":114},"The paper targets early breast cancer detection as a critical challenge for improving outcomes, especially given high mortality and the need for timely diagnosis.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which models are used for classification?",{"text":118,"@type":114},"It employs three deep CNN base classifiers: VGG-11, Inception v3, and ResNet50, trained as part of a transfer-learning based framework.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the proposed fuzzy ensemble work?",{"text":122,"@type":114},"The approach builds a fuzzy ranking of the base models using a modified Gompertz function, then adaptively integrates the decision scores to produce final predictions.","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},338152,1790551085,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"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":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Sannasi Chakravarthy et al.  \nBMC Medical Imaging (2024) 24:82  \n[https://doi.org/10.1186/s12880-024-01267-8](https://doi.org/10.1186/s12880-024-01267-8)  \nBMC Medical Imaging  \n RESEARCH Open Access  \nDeep transfer learning with fuzzy ensemble   approach for the early detection of breast cancer  \nS. R. Sannasi Chakravarthy1, N. Bharanidharan2, V. Vinoth Kumar2, T. R. Mahesh3, Mohammed S. Alqahtani4 and Suresh Guluwadi5*  \nAbstract  \nBreast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98. 986% .  \nKeywords Deep learning, Fuzzy ranking, Convolution neural network, Transfer learning  \nIntroduction  \nBeing a cancer type provides a higher mortality rate among women, breast cancer is a highly incident cancer type next to lung cancer [1] . The incidence of this cancer type is more found in men rather than women.  \n*Correspondence:  \nSuresh Guluwadi [suresh.guluwadi@astu.edu.et](suresh.guluwadi@astu.edu.et)  \n1 Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India  \n2 School of Computer Science Engineering and Information systems, Vellore Institute of Technology, Vellore 632014, India  \n3 Department of Computer Science and Engineering JAIN (Deemed-tobe University), Bengaluru 562112, India  \n4 Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia  \n5 Adama Science and Technology University, Adama 302120, Ethiopia  \nIn the year 2020, over 2.3 million global women remained identified with this cancer type for diagnosis and approximately 6,85,000 have died [2] . Accordingly, somewhere in all parts of the globe, for every fourteen seconds, a woman is getting a diagnosis of this cancer type. This makes this cancer type as the most common type of disease among females in both developed as well as in developing countries. Additionally in the year of 2012, breast cancer was reported in around twelve percent of all newer cancer issues and twenty-five percentage of all cancer types among women [2] . As per the global reports, out of 184 nations, breast cancer is the most recurrently diagnosed illness among women in 140 nations [2] . Due to this, it is the most deadly cancer type among global women. Also, the above  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unl","cbCaivsPIAZ17JyO","https://ap.wps.com/l/cbCaivsPIAZ17JyO","pdf",2612607,15,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper targets early breast cancer detection as a critical challenge for improving outcomes, especially given high mortality and the need for timely diagnosis.\"},{\"question\":\"Which models are used for classification?\",\"answer\":\"It employs three deep CNN base classifiers: VGG-11, Inception v3, and ResNet50, trained as part of a transfer-learning based framework.\"},{\"question\":\"How does the proposed fuzzy ensemble work?\",\"answer\":\"The approach builds a fuzzy ranking of the base models using a modified Gompertz function, then adaptively integrates the decision scores to produce final predictions.\"}]","Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer | PDF",1790022236,38]