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Manual analysis of structurally complex CRC histopathology images is slow, labor-intensive, and error-prone. The study develops an automated deep learning framework for CRC classification, combining domain-specific transfer learning with multi-model feature fusion. A multi-head self-attention mechanism fuses features from multiple pretrained models, followed by an MLP classifier for prediction. Evaluation on EBHI, Chaoyang, and COAD shows 99.68% accuracy on EBHI, 86.72% on Chaoyang, and 99.44% on COAD, indicating strong generalization and potential to support pathologists.",{"@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/histopathological-classification-of-colorectal-cancer-based-on-domain-specific-transfer-learning-and-multimodel-feature-fusion/372731/",{"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/histopathological-classification-of-colorectal-cancer-based-on-domain-specific-transfer-learning-and-multimodel-feature-fusion/372731.png","ImageObject",300,407,{"name":92,"@type":93},"Mary Man","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the proposed approach address for colorectal cancer diagnosis?","Question",{"text":113,"@type":114},"It targets the time-consuming and error-prone manual interpretation of complex colorectal cancer histopathology images by building an automated classification framework.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the model fuse information from different sources?",{"text":118,"@type":114},"It uses a multi-head self-attention mechanism to fuse features extracted from multiple pretrained models, then applies an MLP classifier for final prediction.",{"name":120,"@type":111,"acceptedAnswer":121},"Which datasets were used to evaluate the framework and what were the reported accuracies?",{"text":122,"@type":114},"The model was evaluated on EBHI, Chaoyang, and COAD, achieving 99.68% on EBHI, 86.72% on Chaoyang, and 99.44% on COAD.","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},372731,1790388840,{"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},7421720224475,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nHistopathological classification of colorectal cancer based on domainspecific transfer learning and multimodel feature fusion  \nQi Ke1,2,3, Yan Chai Hum2􀀍, Wun-She Yap3, Tian SweeTan4, Humaira Nisar5, Hamam Mokayed6, AiQuan Li1, Rong Gao1 & YuJianGan7  \nColorectal cancer (CRC) poses a significant global health burden, where early and accurate diagnosis is vital to improving patient outcomes. However, the structural complexity ofCRC histopathological images renders manual analysis time-consuming and error-prone. This study aims to develop an automated deep learning framework that enhances classification accuracy and efficiency in CRC diagnosis. The proposed model integrates domain-specific transfer learning and multi-model feature fusion to address challenges such as multi-scale structures, noisy labels, class imbalance, and finegrained subtype classification. The model first applies domain-specific transfer learning to extract highly relevant features from histopathological images. A multi-head self-attention mechanism then fuses features from multiple pre-trained models, followed by a multilayer perceptron (MLP) classifier for final prediction. The framework was evaluated on three publicly available CRC datasets: EBHI, Chaoyang, and COAD. The model achieved a classification accuracy of 99.68% on the EBHI dataset (200 × subset), 86.72% on the Chaoyang dataset, and 99.44% on the COAD dataset. These results demonstrate strong generalization across diverse and complex histopathological image conditions. This study highlights the effectiveness of combining domain-specific transfer learning with multimodel feature fusion and attention mechanisms for CRC classification. The proposed model offers a reliable and efficient tool to support pathologists in diagnostic workflows, with the potential to reduce manual workload and improve diagnostic consistency.  \nKeywords Histopathological classification, Colorectal cancer, Transfer learning, Domain-specific models, Feature fusion  \nColorectal cancer (CRC) is one of the most prevalent and deadly malignancies worldwide, with over 1.85 million new cases and 850,000 deaths annually1. By 2040, the global burden of CRC is projected to reach 3.2 million cases. Histopathological examination remains the gold standard for CRC diagnosis, relying heavily on pathologists’ subjective interpretation of tissue samples. However, this process is time-consuming, laborintensive, and prone to human error2. The development of Computer-Aided Diagnosis (CAD) systems for automated histopathological image classification is therefore critical to improving diagnostic efficiency and accuracy, enabling timely treatment planning3.  \nRecent works have shown that deep learning, especially transfer learning and ensemble learning, can significantly improve classification performance in cancer histopathology, including breast and gastric cancers4. Despite significant advancements in deep learning, particularly convolutional neural networks (CNNs), the  \n1School of Big Data and Artificial Intelligence, Guangxi University of Finance and Economics, Nanning, China.  \n2Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia. 3Department of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kampar, Malaysia. 4Department of Biomedical Engineering & Health Sciences, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310 Johor Bahru, Johor, Malaysia. 5Department of Electronic Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Malaysia. 6Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Luleå, Sweden. 7School of Electronics, Electrical Engineering and Computer Scienc","cbCaiooV8AIkHMXt","https://ap.wps.com/l/cbCaiooV8AIkHMXt","pdf",3966201,22,"English","# Overview\n# Proposed Framework\n## Domain-specific transfer learning\n## Multi-model feature fusion with attention\n# Evaluation Results\n# Contributions and Motivation","[{\"question\":\"What problem does the proposed approach address for colorectal cancer diagnosis?\",\"answer\":\"It targets the time-consuming and error-prone manual interpretation of complex colorectal cancer histopathology images by building an automated classification framework.\"},{\"question\":\"How does the model fuse information from different sources?\",\"answer\":\"It uses a multi-head self-attention mechanism to fuse features extracted from multiple pretrained models, then applies an MLP classifier for final prediction.\"},{\"question\":\"Which datasets were used to evaluate the framework and what were the reported accuracies?\",\"answer\":\"The model was evaluated on EBHI, Chaoyang, and COAD, achieving 99.68% on EBHI, 86.72% on Chaoyang, and 99.44% on COAD.\"}]","Histopathological classification of colorectal cancer based on domain-specific transfer learning and multimodel feature fusion | PDF",1790195239,55]