[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-371082-105":59,"doc-detail-371082-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","hybrid-deep-learning-with-attention-fusion-for-enhanced-colon-cancer-detection","Hybrid deep learning with attention fusion for enhanced colon cancer detection","","Hybrid deep learning model for automated colon cancer detection integrates EfficientNet-B3 and Vision Transformer using an Attention Fusion mechanism on the Kvasir endoscopic dataset. EfficientNet-B3 captures fine-grained local textures while the Vision Transformer models global contextual relationships. A multi-head attention fusion block produces comprehensive representations and improves classification stability. Optimization relies on Matthews Correlation Coefficient (MCC) alongside accuracy, F1-score, and Brier Score. Results reach 96.2% accuracy and MCC 0.961, outperforming baselines with cross-validation support.",{"@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/hybrid-deep-learning-with-attention-fusion-for-enhanced-colon-cancer-detection/371082/",{"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/hybrid-deep-learning-with-attention-fusion-for-enhanced-colon-cancer-detection/371082.png","ImageObject",300,407,{"name":92,"@type":93},"LangkahRina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the proposed model combine EfficientNet-B3 and Vision Transformer?","Question",{"text":112,"@type":113},"It fuses EfficientNet-B3 local texture features with Vision Transformer global contextual relationships using a multi-head attention-based fusion block.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which dataset is used to train and evaluate the model?",{"text":117,"@type":113},"The model is trained and evaluated using the Kvasir endoscopic dataset, referenced as a benchmark for colon cancer detection.",{"name":119,"@type":110,"acceptedAnswer":120},"What metrics guide optimization and performance assessment?",{"text":121,"@type":113},"Optimization is guided by the Matthews Correlation Coefficient (MCC), and performance is assessed using accuracy, F1-score, and Brier Score.","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},371082,1790409542,{"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":24,"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},962090893776,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nHybrid deep learning with attention fusion for enhanced colon cancer detection  \nSüheyla Demirtaş Alpsalaz1, Emrah Aslan2, Yıldırım Özüpak3, Feyyaz Alpsalaz4  \n,  \nHasan Uzel4 & Viktoria Bereznychenko5􀀍  \nThis study introduces a hybrid deep learning model integrating EfficientNet-B3 and Vision Transformer with an Attention Fusion mechanism for automated colon cancer detection using the Kvasir endoscopic dataset. The model leverages EfficientNet-B3’s strength in capturing fine-grained local textures and Vision Transformer’s ability to model global contextual relationships. A multi-head attention-based fusion block harmonizes these features, achieving comprehensive representations and enhanced classification stability. Model optimization was guided by the Matthews Correlation Coefficient (MCC), alongside evaluations of accuracy, F1-score, and Brier Score. Experimental results demonstrate  \na 96.2% accuracy and an MCC of 0.961, surpassing standalone baselines and existing benchmark architectures. Cross-validation confirmed robust generalization, while Grad-CAM analyses improved interpretability by visualizing salient histopathological regions influencing predictions. Despite slight overfitting tendencies, the model maintained strong performance across all eight image classes. These findings highlight the model’s ability to address limitations of single-architecture approaches by combining local and global feature extraction, offering rapid, objective, and reliable diagnostic support. The proposed framework shows significant promise for integration into computer-aided colonoscopy systems, paving the way for enhanced clinical diagnostics and reduced pathologist workload through AI-driven precision medicine.  \nKeywords Colon cancer, Deep learning, Hybrid model, EfficientNet-B3, Vision transformer  \nColorectal cancer (CRC) is one of the most common and lethal cancers worldwide1. Its early diagnosis and accurate classification are essential for improving patient survival and guiding effective treatment strategies2. Traditional diagnostic approaches rely heavily on manual histopathological examination, which is timeconsuming and subject to inter-observer variability3. In recent years, machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools in medical image analysis and clinical decision-making4. These technologies can automatically process and interpret complex histopathological and molecular data, identifying subtle patterns that may not be visible to the human eye. AI-based diagnostic systems, particularly those using deep learning and convolutional neural networks (CNNs), have shown high performance in differentiating cancerous from non-cancerous tissues5. ML algorithms such as support vector machines6, random forests, and ensemble models further contribute to improving classification accuracy and robustness7. The integration of these computational approaches into colorectal cancer research enables earlier detection8, more consistent diagnosis, and better prediction of disease progression9. As AI and ML continue to evolve, their applications in colorectal cancer diagnostics are expected to play a vital role in advancing precision medicine and reducing the global burden of this disease10.  \nRecent advances in deep learning (DL) and machine learning (ML) have achieved significant success in the automated analysis of histopathological images11. Convolutional Neural Networks (CNNs), Vision Transformers (ViTs) and hybrid models used for feature extraction and classification tasks provide significant advantages over traditional methods in terms of sensitivity, specificity and overall diagnostic performance12. The ability of DL models to learn complex patterns and subtle morphological differences in histopathologic images provides an important foundation for the development of robust and scalable automated colon cancer detection sy","cbCaik90A4QNgmRI","https://ap.wps.com/l/cbCaik90A4QNgmRI","pdf",3502110,17,"English","# Introduction\n## Background on colorectal cancer and diagnostic needs\n## Role of AI and machine learning in histopathology","[{\"question\":\"How does the proposed model combine EfficientNet-B3 and Vision Transformer?\",\"answer\":\"It fuses EfficientNet-B3 local texture features with Vision Transformer global contextual relationships using a multi-head attention-based fusion block.\"},{\"question\":\"Which dataset is used to train and evaluate the model?\",\"answer\":\"The model is trained and evaluated using the Kvasir endoscopic dataset, referenced as a benchmark for colon cancer detection.\"},{\"question\":\"What metrics guide optimization and performance assessment?\",\"answer\":\"Optimization is guided by the Matthews Correlation Coefficient (MCC), and performance is assessed using accuracy, F1-score, and Brier Score.\"}]","Hybrid deep learning with attention fusion for enhanced colon cancer detection | PDF",1790191204,43]