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The workflow uses CNN models (ADa-22 and AD-22), transformer networks, and an SVM classifier, applied to the CVC ClinicDB dataset with 1650 colonoscopy images labeled as polyps or non-polyps. The AD-22 + Transformer + SVM ensemble achieves AUC 0.99, training accuracy 99.50%, and testing accuracy 99.00%, with high recall for both classes. K-means clustering supports bounding-box visualization and improves segmentation.",{"@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/colorectal-cancer-detection-with-enhanced-precision-using-a-hybrid-supervised-and-unsupervised-learning-approach/364347/",{"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/colorectal-cancer-detection-with-enhanced-precision-using-a-hybrid-supervised-and-unsupervised-learning-approach/364347.png","ImageObject",300,407,{"name":92,"@type":93},"Lucas Martin","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","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 hybrid methods are used for colorectal cancer detection in the study?","Question",{"text":112,"@type":113},"The approach integrates supervised classification and unsupervised clustering within a hybrid ensemble framework. It combines CNN models, transformer networks, and an SVM classifier, supported by K-means clustering for segmentation enhancement.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which dataset and class labels support model training and testing?",{"text":117,"@type":113},"The work uses the CVC ClinicDB dataset containing 1650 colonoscopy images classified as polyps or non-polyps. Performance is reported separately for both categories.",{"name":119,"@type":110,"acceptedAnswer":120},"What results does the best ensemble model achieve?",{"text":121,"@type":113},"The best-performing ensemble is AD-22 + Transformer + SVM, reporting AUC 0.99, training accuracy 99.50%, and testing accuracy 99.00%. It also reports high accuracy and recall for both polyps and non-polyps.","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},364347,1790451693,{"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},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nColorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach  \nAkella S. Narasimha Raju1􀀍, K. Venkatesh2􀀍, Ranjith Kumar Gatla3,  \nEswara Prasad Konakalla4, Marwa M. Eid5, Nataliia Titova6􀀍, SherifS. M. Ghoneim7 &  \nRamy N. R. Ghaly8,9  \nThe current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVMclassifier, all inbuilt. The CVC ClinicDB dataset supports this process, containing 1650 colonoscopy images classified as polyps or non-polyps. The best performance in the ensembles was done by the AD-22 +Transformer + SVM model, with anAUC of 0.99, a training accuracy of 99.50%, and a testing accuracy of 99.00%. This group also saw a high accuracy of 97.50% for Polyps and 99.30% for NonPolyps, together with a recall of 97.80% for Polyps and 98.90% for Non-Polyps, hence performing very well in identifying both cancerous and healthy regions. The framework proposed here uses  \nK-means clustering in combination with the visualisation of bounding boxes, thereby improving segmentation and yielding a silhouette score of 0.73 with the best cluster configuration. It discusses how to combine feature interpretation challenges into medical imaging for accurate localization and precise segmentation of malignant regions. A good balance between performance and generalization shall be done by hyperparameter optimization-heavy learning rates; dropout rates and overfitting shall be suppressed effectively. The hybrid schema of this work treats the deficiencies of the previous approaches, such as incorporating CNN-based effective feature extraction, Transformer networks for developing attention mechanisms, and finally the fine decision boundary of the support vector machine. Further, we refine this process via unsupervised clustering for the purpose of enhancing the visualisation of such a procedure. Such a holistic framework, hence, further boosts classification and segmentation results by generating understandable outcomes for more rigorous benchmarking of detecting colorectal cancer and with higher reality towards clinical application feasibility.  \nKeywords Colorectal cancer, Integrated CNNs, Transformers, Support vector machines, K-Means clustering  \nCRC is one of the most common kinds of cancer and, on the whole, remains an important health-care problem because mortality in such high quantities remains associated with this type of cancer. According to our estimate, in 2024, the world will register 2.2 million new cases and 1.1 million deaths due to CRC. High rates ofCRC have been reported in countries with high incomes: Australia, New Zealand, and parts of Europe1. There are about  \n1Department of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Dundigul, Hyderabad, Telangana 500043, India. 2Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamilnadu 603203, India. 3Department of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Dundigul, Hyderabad, Telangana 500043, India. 4Department of Physics and Electronics, B.V. Raju College, Bhimavaram, Garagaparru Road, Kovvada, Andhra Pradesh 534202, India. 5College of Applied Medical Science, Taif University, 21944 Taif, Saudi Arabia. 6Biomedical Engineering Department, National University Odesa Polytechnic, Odesa 65044, Ukraine. 7Department of Electrical Engineering, College of Engineering, Taif University, 21944 Taif, Saudi Arabia. 8Ministry of Higher Education, Mataria Technical College, Cairo 11718, Egy","cbCaiiaNmQOU8VSD","https://ap.wps.com/l/cbCaiiaNmQOU8VSD","pdf",6750491,37,"English","# Overview\n## Hybrid framework for detection and segmentation\n## Dataset and evaluation results\n## Segmentation support via clustering and visualization\n## Clinical feasibility and optimization considerations","[{\"question\":\"What hybrid methods are used for colorectal cancer detection in the study?\",\"answer\":\"The approach integrates supervised classification and unsupervised clustering within a hybrid ensemble framework. It combines CNN models, transformer networks, and an SVM classifier, supported by K-means clustering for segmentation enhancement.\"},{\"question\":\"Which dataset and class labels support model training and testing?\",\"answer\":\"The work uses the CVC ClinicDB dataset containing 1650 colonoscopy images classified as polyps or non-polyps. Performance is reported separately for both categories.\"},{\"question\":\"What results does the best ensemble model achieve?\",\"answer\":\"The best-performing ensemble is AD-22 + Transformer + SVM, reporting AUC 0.99, training accuracy 99.50%, and testing accuracy 99.00%. It also reports high accuracy and recall for both polyps and non-polyps.\"}]","Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach | PDF",1790157126,93]