[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-328285-105":59,"doc-detail-328285-en":131},{"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":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","colon-cancer-diagnosis-by-means-of-explainable-deep-learning","Colon cancer diagnosis by means of explainable deep learning","","Explainable deep learning is used for early detection of adenocarcinoma in colon tissue by classifying histological images and producing visual explainability that highlights, on tissue images, the areas most related to cancer presence from the model’s perspective. Multiple deep learning architectures are evaluated to select the best option through quantitative and qualitative results based on prediction explainability. Experiments on 10,000 colon tissue images demonstrate effectiveness with an accuracy of 0.99, supporting cancer detection and localization from tissue images.",{"@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":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/colon-cancer-diagnosis-by-means-of-explainable-deep-learning/328285/",{"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/colon-cancer-diagnosis-by-means-of-explainable-deep-learning/328285.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the method detect colon cancer from tissue images?","Question",{"text":113,"@type":114},"It classifies histological images using deep learning and provides visual explainability that highlights tissue regions linked to cancer presence according to the model’s prediction.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Why is explainability important in colon cancer diagnosis models?",{"text":118,"@type":114},"Many deep neural networks behave like “black boxes,” and the paper emphasizes explainability to make decisions more understandable and suitable for practical clinical adoption.",{"name":120,"@type":111,"acceptedAnswer":121},"What experimental results are reported for the proposed approach?",{"text":122,"@type":114},"Experiments on 10,000 colon tissue images show effectiveness, achieving an accuracy of 0.99 for cancer detection and localization.","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},328285,1790218913,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"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":105,"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},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nColon cancer diagnosis by means of explainable deep learning  \nMarcello Di Giammarco2,3*, Fabio Martinelli2, Antonella Santone1, Mario Cesarelli4 & Francesco Mercaldo1,2  \nEarly detection ofthe adenocarcinoma cancer in colon tissue by means of explainable deep learning, by classifying histological images and providing visual explainability on model prediction. Considering that in recent years, deep learning techniques have emerged as powerful techniques in medical image analysis, offering unprecedented accuracy and efficiency, in this paper we propose a method to automatically detect the presence of cancerous cells in colon tissue images. Various deep learning architectures are considered, with the aim of considering the best one in terms of quantitative and qualitative results. As a matter of fact, we consider qualitative results by taking into account the so-called prediction explainability, by providing a way to highlight on the tissue images the areas that from the model point of view are related to the presence of colon cancer. The experimental analysis, performed on 10,000 colon issue images, showed the effectiveness of the proposed method by obtaining an accuracy equal to 0.99. The experimental analysis shows that the proposed method can be successfully exploited for colon cancer detection and localisation from tissue images.  \nKeywords Colon, Cancer, Deep learning, Machine learning, Explainability, Artificial intelligence  \nColon cancer, also known as colorectal cancer, is a type of cancer that begins in the cells of the colon, which is apart of the large intestine. It typically starts as small, noncancerous clumps of cells called adenomatous polyps. Over time, some of these polyps can become cancerous1.  \nColon cancer is one of the most common forms of cancer worldwide. The American Cancer Society (https:// [www.cancer.org/cancer/types/colon-rectal-cancer/about/key-statistics.html](www.cancer.org/cancer/types/colon-rectal-cancer/about/key-statistics.html)) estimates for the number of colorectal cancers in the United States for 2024 are: About 106,590 new cases of colon cancer (54,210 in men and 52,380 in women). Risk factors for developing colon cancer include age, family history of colorectal cancer, personal history of colorectal polyps or inflammatory bowel disease, certain genetic conditions, a diet high in red or processed meats, lack of physical activity, obesity, smoking, and heavy alcohol use.  \nSymptoms of colon cancer may include changes in bowel habits, persistent abdominal discomfort, unexplained weight loss, fatigue, and rectal bleeding. However, in the early stages, colon cancer may not cause noticeable symptoms, making regular screening important for early detection.  \nScreening for colon cancer often involves tests such as colonoscopy, sigmoidoscopy, and fecal occult blood tests. Early detection2 is crucial in terms of reducing mortality rates and successful treatment. Early-stage cancers are typically smaller and confined to the inner layers of the colon, making them more amenable to curative treatment options such as surgery, radiation therapy, and chemotherapy. Furthermore, early-stage colon cancer may require less aggressive treatment compared to advanced-stage disease.  \nAdopting a healthy lifestyle, including a balanced diet, regular exercise, and avoiding known risk factors, can contribute to the prevention of colon cancer. Regular screenings are especially important for individuals with risk factors or those over the age of 50, as the risk of developing colon cancer increases with age.  \nDiagnosing colon cancer typically involves a combination of medical history evaluation, physical examination, and various diagnostic tests. The common method used for diagnosing colon cancer is represented by colonoscopy. During this procedure, a flexible tube with a camera on the end (i.e., the colonoscope) is inserted through the ","cbCaiosZv6BvbvV7","https://ap.wps.com/l/cbCaiosZv6BvbvV7","pdf",1944413,"English","# Abstract\n## Early detection approach\n## Model architectures and evaluation\n## Experimental results and accuracy\n## Clinical context and need for explainability\n## Colon cancer overview and risk factors\n## Symptoms and importance of screening\n## Diagnostic workflow and biopsy confirmation","[{\"question\":\"How does the method detect colon cancer from tissue images?\",\"answer\":\"It classifies histological images using deep learning and provides visual explainability that highlights tissue regions linked to cancer presence according to the model’s prediction.\"},{\"question\":\"Why is explainability important in colon cancer diagnosis models?\",\"answer\":\"Many deep neural networks behave like “black boxes,” and the paper emphasizes explainability to make decisions more understandable and suitable for practical clinical adoption.\"},{\"question\":\"What experimental results are reported for the proposed approach?\",\"answer\":\"Experiments on 10,000 colon tissue images show effectiveness, achieving an accuracy of 0.99 for cancer detection and localization.\"}]","Colon cancer diagnosis by means of explainable deep learning | PDF",1789979177,28]