[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-332705-105":59,"doc-detail-332705-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","enhanced-convolutional-neural-network-architecture-optimized-by-improved-chameleon-swarm-algorithm-for-melanoma-detection-using-dermatological-images","Enhanced convolutional neural network architecture optimized by improved chameleon swarm algorithm for melanoma detection using dermatological images","","Early skin cancer detection is critical to patient recovery and survival, yet manual melanoma diagnosis from dermoscopy images is slow, costly, and vulnerable to human error. A deep learning framework is proposed that preprocesses dermoscopy images to remove artifacts, enhance contrast, and suppress noise, then feeds them into an optimized CNN. The CNN is trained using the Improved Chameleon Swarm Algorithm to improve performance. Experiments on the SIIM-ISIC Melanoma dataset validate results with rigorous evaluation metrics, showing accurate melanoma diagnosis potential for clinical support.",{"@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/enhanced-convolutional-neural-network-architecture-optimized-by-improved-chameleon-swarm-algorithm-for-melanoma-detection-using-dermatological-images/332705/",{"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/enhanced-convolutional-neural-network-architecture-optimized-by-improved-chameleon-swarm-algorithm-for-melanoma-detection-using-dermatological-images/332705.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","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},"Why is early melanoma detection important in this work?","Question",{"text":113,"@type":114},"The document states that early detection and treatment improve patient recovery and survival. It also notes that recognizing warning signs helps identify cancerous changes before spread.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed approach process dermoscopy images?",{"text":118,"@type":114},"It preprocesses dermoscopy images to remove artifacts, enhance contrast, and cancel noise before feeding them into the CNN.",{"name":120,"@type":111,"acceptedAnswer":121},"How is the CNN optimized for melanoma detection?",{"text":122,"@type":114},"The CNN is trained using the Improved Chameleon Swarm Algorithm (CSA) as an innovative metaheuristic to optimize performance.","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},332705,1790385118,{"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":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,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nEnhanced convolutional neural network architecture optimized by improved chameleon swarm algorithm for melanoma detection using dermatological images  \nWeiqi Wu1,4, Liuyan Wen1,4, Shaoping Yuan1, Xiuyi Lu1􀀍, Juan Yang1􀀍 & Asad Rezaei sofla2,3􀀍  \nEarly detection and treatment of skin cancer are important for patient recovery and survival. Dermoscopy images can help clinicians for timely identification of cancer, but manual diagnosis is time-consuming, costly, and prone to human error. To conduct this, an innovative deep learning-based approach has been proposed for automatic melanoma detection. The proposed method involves preprocessing dermoscopy images to remove artifacts, enhance contrast, and cancel noise, followed by feeding them into an optimized Convolutional Neural Network (CNN). The CNN is trained using an innovative metaheuristic called the Improved Chameleon Swarm Algorithm (CSA) to optimize its performance. The approach has been validated using the SIIM-ISIC Melanoma dataset and the results have been confirmed through rigorous evaluation metrics. Simulation results demonstrate the efficacy of the proposed method in accurately diagnosing melanoma from dermoscopy images by highlighting its potential as a valuable tool for clinicians in early cancer detection.  \nKeywords Melanoma, Skin Cancer, Medical Imaging, Dermoscopy, Early Detection, CNN, Improved Chameleon Swarm Algorithm  \nWhen the body requires more cells, they naturally proliferate and expand. However, owing to genetic alterationsand mutations in cell structure, they begin to grow and proliferate abnormally, resulting in cancer. A huge mass of cells is formed as a result of this irregular development1. This mass is toxic to the body and depletes its resources, attacking and killing tissues everywhere around it2. Each country has different cancer prevalence rates. Although, some malignancies, such as skin cancer, are, of course, found all over the world3. Exposure to sunlight causes abnormal growth of skin cells, called skin cancer4. In fact, the cause of skin cancer is that a mutation (an error) appears in the DNA of skin cells5. A mass of cancer cells is generated. Skin cancer can also occur in areas of the skin that are not normally exposed to sunlight. Avoiding or limiting exposure to Ultraviolet (UV) radiation can decrease the risk of skin cancer6.  \nThe thin layer that acts as a protective covering for skin cells is called the epidermis. It should be noted that the body constantly sheds skin cells7–9. The epidermis consists of three major cell kinds around Squamous cells that are placed right below the outward surface and operate as the inner lining of the skin10.  \nBasal cells that produce novel skin cells are found under the squamous cells. Melanocytes that produce melanin. Melanin is a pigment which provides the skin its natural color and is positioned in the lower portion of the epidermis11. Melanocytes generate more melanin once the body is exposed to sunlight to assist in shielding the deeper layers of the skin. This is why the skin darkens in the sun.  \nThe most severe kind of skin cancer is melanoma which occurs in melanocyte cells that produce melanin. It can even be created within the eyes and seldom in internal organs like the intestines. The precise reason of it is still unknown; however, being exposed to diffuse UV light from sunlight or solarium lamps and beds raises the danger of evolving melanoma. Restraining UV exposure can assist in decreasing the danger of melanoma.  \n1Department of Dermatology, The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou 511300, People’s Republic of China. 2University of Tehran, Tehran, Iran. 3College of Technical Engineering, The Islamic University, Najaf, Iraq. 4Weiqi Wu and Liuyan Wen are co-first authors. 􀀍 email: [714373606@qq.com](714373606@qq.com);  \n[yjuan0623@163.com](yjuan0623@163.com); [asadrezaeisof","cbCaibNK1vtbRU71","https://ap.wps.com/l/cbCaibNK1vtbRU71","pdf",3961558,18,"English","# Background\n## Motivation for early melanoma detection\n# Proposed Method\n## Dermoscopy preprocessing\n## Optimized CNN with Improved Chameleon Swarm Algorithm\n# Validation and Results\n## Dataset and evaluation metrics\n## Diagnostic effectiveness","[{\"question\":\"Why is early melanoma detection important in this work?\",\"answer\":\"The document states that early detection and treatment improve patient recovery and survival. It also notes that recognizing warning signs helps identify cancerous changes before spread.\"},{\"question\":\"How does the proposed approach process dermoscopy images?\",\"answer\":\"It preprocesses dermoscopy images to remove artifacts, enhance contrast, and cancel noise before feeding them into the CNN.\"},{\"question\":\"How is the CNN optimized for melanoma detection?\",\"answer\":\"The CNN is trained using the Improved Chameleon Swarm Algorithm (CSA) as an innovative metaheuristic to optimize performance.\"}]","Enhanced convolutional neural network architecture optimized by improved chameleon swarm algorithm for melanoma detection using dermatological images | PDF",1789999145,45]