[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-371947-105":59,"doc-detail-371947-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","explainable-ai-based-hybrid-drm-net-transfer-learning-model-for-breast-cancer-detection-and-classification-using-ultrasound-images","Explainable AI based hybrid DRM-Net transfer learning model for breast cancer detection and classification using ultrasound images","","Breast cancer is a major cause of cancer-related mortality among women worldwide, making early and accurate detection essential for effective treatment. A hybrid explainable AI framework named DRM-Net is developed for ultrasound image-based breast cancer detection and classification using transfer learning. Six baseline transfer learning models are trained, validated, and tested, while DRM-Net stacks the top three models via concatenation and flattening of deep dense layers. Image preprocessing, masking, augmentation, and hyperparameter tuning are applied, and models are assessed with standard metrics and statistical analysis. XAI interpretation is provided through CAM variants, and DRM-Net achieves accuracy 96.71% with strong precision, recall, F1-score, and AUC, outperforming competing studies, with potential clinical decision support and applicability to other ultrasound-accessible diseases.",{"@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/explainable-ai-based-hybrid-drm-net-transfer-learning-model-for-breast-cancer-detection-and-classification-using-ultrasound-images/371947/",{"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/explainable-ai-based-hybrid-drm-net-transfer-learning-model-for-breast-cancer-detection-and-classification-using-ultrasound-images/371947.png","ImageObject",300,407,{"name":92,"@type":93},"RuangKosong","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-09-23",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 is the proposed hybrid model DRM-Net and how is it built?","Question",{"text":112,"@type":113},"DRM-Net is a hybrid explainable AI transfer learning model formed by stacking the top three transfer learning models using concatenation and flattening of deep dense layers.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which methods are used to improve model performance for ultrasound images?",{"text":117,"@type":113},"The approach incorporates image preprocessing, image masking, data augmentation, and hyperparameter tuning to enhance overall performance.",{"name":119,"@type":110,"acceptedAnswer":120},"How is decision-making transparency achieved in the proposed method?",{"text":121,"@type":113},"The model uses XAI-based class activation mapping (CAM) with different variants to interpret and visualize the decision basis.","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},371947,1790489447,{"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},962090883219,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nExplainable AI based hybrid DRM-Net transfer learning model for breast cancer detection and classification using ultrasound images  \nShahid Mohammad Ganie1􀀍, Majid Bashir Malik2, MirAadil3, Ghazala Muteeb4, Mohd Farhan5,6 & Mohammad Aatif7􀀍  \nBreast cancer is a serious health concern and one of the leading causes of cancer-related deaths among women worldwide. The early diagnosis of breast cancer is crucial for successful treatment and improved patient outcomes. Advanced computational techniques, particularly deep transfer learning have gained considerable attention for their effectiveness in medical image computing. Initially, six transfer learning models with different specifications are trained, validated and tested on the breast ultrasound image dataset. Additionally, a novel hybrid model, DRM-Net, is developed by stacking the top three TL models based on concatenation and flattening of deep dense layers. Various techniques, including image preprocessing, image masking, data augmentation, and hyperparameter tuning, are incorporated to enhance the overall performance of the considered models. The models are thoroughly evaluated using various standard metrics and statistical methods. Furthermore, to ensure transparent decision-making, the proposed model is interpreted using XAI-based class activation mapping (CAM) method with different versions. The proposed DRM-Net model demonstrated superior predictive performance compared to the other six transfer learning models. Among all the models, DRM-Net achieved the highest overall performance, with an accuracy of 96.71%, precision of 96%, recall of 97%, F1-score of 97%, and anAUC value of 99%, respectively. It also outperformed state-of-the-art similar studies in the literature. The experimental results demonstrate the potential utility of the hybrid DRMNet model in improving the accuracy of breast cancer diagnosis, which could facilitate informed clinical decision-making. The proposed model can also be applied to other diseases where ultrasound imaging is available  \nKeywords Breast cancer prediction, Ultrasound images, Transfer learning, Stacking model, XAI, GradCAM, Grad-CAM + +, Score-CAM, Friedman rank analysis, Post-hoc analysis  \nAccording to the World Health Organisation (WHO), Breast Cancer (BC) is one of the most common types of cancer and has affected millions of people globally ([https://www.who.int/publications/i/item/who-position](https://www.who.int/publications/i/item/who-position)[-paper-on-mammography-screening](-paper-on-mammography-screening)) . In underdeveloped countries, BC rates are significantly higher than in developed countries, particularly among women. Each year, approximately 1.38 million individuals are diagnosed with breast cancer, and unfortunately, one-third of those diagnosed do not survive. The global death rate from cancer is 9.6 million, but there are also 1.7 million individuals who survive after receiving treatment for cancer1,2.  \n1Department of Health Information Management and Technology, College of Applied Medical Sciences, King Faisal University, 31982 Al-Ahsa, Saudi Arabia. 2Department of Computer Sciences, School of Mathematical and Computer Sciences, BGSB University, Rajouri, J&K UT 185234, India. 3Department of Computer Science and Engineering, Model Institute of Engineering and Technology, Jammu, J&K UT, India. 4Department of Nursing, College of Applied Medical Sciences, King Faisal University, 31982 Al-Ahsa, Saudi Arabia. 5Department of Chemistry, College of Science, King Faisal University, 31982 Al-Ahsa, Saudi Arabia. 6Department of Basic Sciences, PreparatoryYear, King Faisal University, 31982 Al-Ahsa, Saudi Arabia. 7Department of Public Health, College of Applied Medical Sciences, King Faisal University, 31982 Al-Ahsa, Saudi Arabia. 􀀍 email: [sganie@kfu.edu.sa](sganie@kfu.edu.sa); [maahmad@kfu.edu.sa](maahmad@kfu.edu.sa)  \n[www. nature.com/scientifi","cbCaibAR1Xqs5wJ2","https://ap.wps.com/l/cbCaibAR1Xqs5wJ2","pdf",5633032,28,"English","# Abstract\n## Model design and transfer learning setup\n## Training enhancements and evaluation\n## Explainable AI interpretation via CAM\n## Comparative performance and clinical relevance","[{\"question\":\"What is the proposed hybrid model DRM-Net and how is it built?\",\"answer\":\"DRM-Net is a hybrid explainable AI transfer learning model formed by stacking the top three transfer learning models using concatenation and flattening of deep dense layers.\"},{\"question\":\"Which methods are used to improve model performance for ultrasound images?\",\"answer\":\"The approach incorporates image preprocessing, image masking, data augmentation, and hyperparameter tuning to enhance overall performance.\"},{\"question\":\"How is decision-making transparency achieved in the proposed method?\",\"answer\":\"The model uses XAI-based class activation mapping (CAM) with different variants to interpret and visualize the decision basis.\"}]","Explainable AI based hybrid DRM-Net transfer learning model for breast cancer detection and classification using ultrasound images | PDF",1790193300,71]