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This paper proposes automated cancer detection using AI-based deep learning, evaluating multiple CNN architectures—including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNet variants, InceptionResNetV2, VGG19, and ResNet152V2—on seven cancer types. Images are segmented and followed by contour feature extraction using perimeter, area, and epsilon, with DenseNet121 achieving the highest validation accuracy (99.94%).",{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automating-cancer-diagnosis-using-advanced-deep-learning-techniques-for-multi-cancer-image-classification/332648/",{"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/automating-cancer-diagnosis-using-advanced-deep-learning-techniques-for-multi-cancer-image-classification/332648.png","ImageObject",300,407,{"name":92,"@type":93},"River Wang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early cancer detection important, and what limits traditional methods?","Question",{"text":112,"@type":113},"Early detection is crucial to reduce mortality, but traditional approaches often depend on invasive procedures and time-consuming analyses, and may not identify multiple cancer types at the same time.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which deep learning models were evaluated for multi-cancer image classification?",{"text":117,"@type":113},"The study evaluates multiple CNNs, including DenseNet121/201, Xception, InceptionV3, MobileNetV2, NASNetLarge/Mobile, InceptionResNetV2, VGG19, and ResNet152V2.",{"name":119,"@type":110,"acceptedAnswer":120},"How are images preprocessed before training and evaluation?",{"text":121,"@type":113},"Images are first segmented, then contour feature extraction is applied where parameters such as perimeter, area, and epsilon are computed.","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},332648,1790198538,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"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,"preview_page_count":4},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAutomating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification  \nYogesh Kumar1, Supriya Shrivastav2, Kinny Garg5, Nandini Modi1􀀍, Katarzyna Wiltos3, Marcin Woźniak3􀀍 & Muhammad Fazal Ijaz4􀀍  \nCancer detection poses a significant challenge for researchers and clinical experts due to its status as the leading cause of global mortality. Early detection is crucial, but traditional cancer detection methods often rely on invasive procedures and time-consuming analyses, creating a demand for more efficient and accurate solutions. This paper addresses these challenges by utilizing automated cancer detection through AI-based techniques, specifically focusing on deep learning models. Convolutional Neural Networks (CNNs), including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNetLarge, NASNetMobile, InceptionResNetV2, VGG19, and ResNet152V2, are evaluated on image datasets for seven types of cancer: brain, oral, breast, kidney, Acute Lymphocytic Leukemia, lung and colon, and cervical cancer. Initially, images undergo segmentation techniques, proceeded by contour feature extraction where parameters such as perimeter, area, and epsilon are computed. The models are rigorously evaluated, with DenseNet121 achieving the highest validation accuracy as 99.94%, 0.0017 as loss, and the lowest Root Mean Square Error (RMSE) values as 0.036056 for training and 0.045826 for validation. These results revealed the capability ofAI-based techniques in improving cancer detection accuracy, with DenseNet121 emerging as the most effective model in this study.  \nKeywords Cancer Detection, Multi-cancer diagnosis, Radiation Therapy, Medical imaging, Noise removal, Deep learning  \nCancer is the foremost cause of global mortality that presents a fearsome challenge for both researchers and medical practitioners1. The intricacies of combating cancer lie in addressing abnormal cell growth that originates from various organs, where the unchecked proliferation of cells becomes inherently controlled. This uncontrollable growth necessitates intervention, whether through physical means such as surgery, medication, hormonal therapy, or radiation therapy, or through natural regression2. In fact, as reported by the American Cancer Society in their 2023 release, anticipated fatalities include 609,820 from oral cancer, 127,070 from lung cancer, 12,470 from skin cancer, 14,890 from kidney cancer, 21,080 from lymphoma cancer, 18,990 from brain cancer, and so on3. Therefore, to save many lives, it is crucial and the top priority to discover cancer at an early stage.  \nThere are more than 150 different types of cancer out of which some are prevalent types such as oral cavity, lip, breast, cervical, as well as thyroid cancers while as few are rare ones like Ewing’s sarcoma, osteosarcoma, male gastrointestinal stromal tumors, breast cancer, mesothelioma, chondrosarcoma, cholangiocarcinoma, adrenocortical carcinoma, pheochromocytoma and paraganglioma, kidney chromophobe carcinoma, sarcoma, as well as ependymoma4. Till now, we still lack the techniques to cure cancers in their early stages although there are visual examinations as well as manual techniques to diagnose various types of cancers. But these traditional cancer detection methods often rely on invasive procedures, time-consuming analyses, and may not be capable of identifying multiple cancer types simultaneously5. Hence, it can be said that manual interpretation is more subjective to biasness and leads to human errors.  \n1Department of Computer Science and Engineering, School of Technology, PDEU, Gandhinagar, Gujarat 382426, India. 2Department ofCSE, AMC Engineering College, Bengaluru, Karnataka, India. 3Faculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44100, Poland. 4School of IT and Engineering, Melbourne Institute of Technology, Melbourne 300","cbCaio6Qhz67m0kW","https://ap.wps.com/l/cbCaio6Qhz67m0kW","pdf",5420041,25,"English","# Introduction\n## Motivation for early and accurate cancer detection\n## Limitations of traditional diagnostic methods\n# Proposed AI Approach\n## Deep learning and machine learning foundations\n## Model architectures evaluated\n# Data and Preprocessing\n## Image segmentation\n## Contour feature extraction\n# Objectives and Evaluation\n## Transfer learning framework\n## Performance results and best model","[{\"question\":\"Why is early cancer detection important, and what limits traditional methods?\",\"answer\":\"Early detection is crucial to reduce mortality, but traditional approaches often depend on invasive procedures and time-consuming analyses, and may not identify multiple cancer types at the same time.\"},{\"question\":\"Which deep learning models were evaluated for multi-cancer image classification?\",\"answer\":\"The study evaluates multiple CNNs, including DenseNet121/201, Xception, InceptionV3, MobileNetV2, NASNetLarge/Mobile, InceptionResNetV2, VGG19, and ResNet152V2.\"},{\"question\":\"How are images preprocessed before training and evaluation?\",\"answer\":\"Images are first segmented, then contour feature extraction is applied where parameters such as perimeter, area, and epsilon are computed.\"}]","Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification | PDF",1789998994,63]