[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-376228-105":59,"doc-detail-376228-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","a-hybrid-deep-learning-model-efficientnet-with-gru-for-breast-cancer-detection-from-histopathology-images","A hybrid deep learning model EfficientNet with GRU for breast cancer detection from histopathology images","","Breast cancer is a major health challenge, and histopathology image analysis is central to early diagnosis, yet many existing models struggle with high-resolution tissue patterns and spatial context, limiting accuracy and generalization. The study proposes a hybrid framework that combines EfficientNetV2 multi-scale feature extraction with a GRU enhanced by an attention mechanism for sequential dependency modeling. Experiments use the BreakHis and Camelyon17 datasets at 200× magnification, reporting precision, recall, F1-score, specificity, IoU, and accuracy. The model achieves 98.15% precision, 95.68% recall, 96.82% F1-score, 96% specificity, 93.99% IoU, and 95.72% accuracy, outperforming AlexNet, DenseNet, MobileNetV3, and EfficientNet.",{"@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/a-hybrid-deep-learning-model-efficientnet-with-gru-for-breast-cancer-detection-from-histopathology-images/376228/",{"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/a-hybrid-deep-learning-model-efficientnet-with-gru-for-breast-cancer-detection-from-histopathology-images/376228.png","ImageObject",300,407,{"name":92,"@type":93},"Grenda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the hybrid EfficientNetV2-GRU model address in breast cancer detection?","Question",{"text":112,"@type":113},"It targets limitations of existing diagnostic models in extracting complex patterns from high-resolution histopathology images and in modeling spatial/contextual dependencies for better generalization.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed architecture combine EfficientNetV2 and GRU?",{"text":117,"@type":113},"EfficientNetV2 is used for multi-scale spatial feature extraction, while a GRU with an attention mechanism models sequential dependencies to capture contextual information.",{"name":119,"@type":110,"acceptedAnswer":120},"Which datasets and evaluation metrics are used to assess performance?",{"text":121,"@type":113},"The model is trained and evaluated on BreakHis and Camelyon17 at 200× magnification, using precision, recall, F1-score, specificity, IoU, and accuracy.","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},376228,1791363190,{"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":29,"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":26},7971474921005,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nA hybrid deep learning model EfficientNet with GRU for breast cancer detection from histopathology images  \nM. Pradeepa1􀀍, B. Sharmila2 & M. Nirmala3  \nBreast cancer remains a significant global health challenge among women, with histopathological image analysis playing a critical role in early detection. However, existing diagnostic models often struggle to extract complex patterns from high-resolution tissue images, limiting their diagnostic accuracy and generalization. This study aims to develop a high-performance deep learning framework for accurate classification of breast cancer in histopathology images, addressing limitations in feature extraction and spatial dependency modeling. A hybrid deep learning model is proposed, integrating EfficientNetV2 for multi-scale feature extraction with a Gated Recurrent Unit (GRU) enhanced by an attention mechanism to model sequential dependencies. The model is trained and evaluated using the BreakHisand Camelyon17 dataset at 200× magnification. Evaluation metrics include precision, recall, F1-score, specificity, Intersection over Union (IoU), and accuracy. The proposed model achieved superior performance compared to existing architectures such as AlexNet, DenseNet, MobileNetV3, and EfficientNet. It attained a precision of 98.15%, recall of 95.68%, F1-score of 96.82%, specificity of 96%, IoU of 93.99%, and accuracy of 95.72% on the test set. The integration of EfficientNetV2 with GRU and attention mechanisms enables effective learning of spatial and contextual features, enhancing the accuracy and interpretability of breast cancer classification from histopathology images. This framework shows strong potential for aiding pathologists in real-time diagnostic workflows.  \nKeywords Breast cancer detection, Hybrid deep learning model, EfficientNetv2, Gated recurrent unit, Attention mechanism  \nBreast cancer ranks as the most frequently diagnosed cancer in women. The World Health Organization (WHO) reports about the increased cases and its impact on individuals, families, and healthcare systems. Breast cancer is highly heterogeneous due to its subtypes and variations in progression rates. This makes the diagnosis to difficult and generalizes the treatments. The increasing rate of breast cancer indicates an urgent need for effective diagnostic tools. So that accurate detection of disease in its early stages can be performed. Early detection significantly improves survival rates through effective treatments that include surgery, chemotherapy, radiation therapy, and hormonal treatments. The survival rate for breast cancer detected at first stage is significantly higher than for cancer diagnosed at later stages.  \nTraditional methods like mammography, ultrasound, and biopsy are used for breast cancer detection. Unfortunately, each method has limitations like false positives, high costs, and limited availability. As breast cancer develop and progress without visible symptoms, depending on physical examinations and conventional diagnostic methods is insufficient for early detection. Innovative diagnostic approaches are needed for improved accuracy, efficiency, and accessibility. To overcome this, digital pathology and artificial intelligence (AI) are integrated into diagnosis and attained promising results. AI based detection systems for processing histopathology images enhances the precision and diagnosis speed. This helps the clinicians to detect even minor patterns which are not visible during manual evaluations1.  \nMachine learning (ML) is an important approach in medical image analysis. Complex features and its importance in breast cancer detection are effectively handled through ML based approaches2. Various ML models such as k-nearest neighbors (k-NN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF)  \n1Department of Electronics and Communication Engineering, SNS College of Technology, Coimba","cbCaibFhNQrDCZb0","https://ap.wps.com/l/cbCaibFhNQrDCZb0","pdf",5881612,24,"English","# Background and motivation\n## Challenges in traditional diagnostic methods\n## Role of machine learning in medical imaging\n## Role of deep learning in histopathology analysis\n# Proposed hybrid model and training setup\n## EfficientNetV2 feature extraction\n## GRU with attention for sequential dependencies\n## Datasets and evaluation metrics\n# Experimental results and comparison\n## Performance on the test set\n## Comparison with existing architectures","[{\"question\":\"What problem does the hybrid EfficientNetV2-GRU model address in breast cancer detection?\",\"answer\":\"It targets limitations of existing diagnostic models in extracting complex patterns from high-resolution histopathology images and in modeling spatial/contextual dependencies for better generalization.\"},{\"question\":\"How does the proposed architecture combine EfficientNetV2 and GRU?\",\"answer\":\"EfficientNetV2 is used for multi-scale spatial feature extraction, while a GRU with an attention mechanism models sequential dependencies to capture contextual information.\"},{\"question\":\"Which datasets and evaluation metrics are used to assess performance?\",\"answer\":\"The model is trained and evaluated on BreakHis and Camelyon17 at 200× magnification, using precision, recall, F1-score, specificity, IoU, and accuracy.\"}]","A hybrid deep learning model EfficientNet with GRU for breast cancer detection from histopathology images | PDF",1790217773]