[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-329937-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-329937-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","developing-an-efficient-method-for-melanoma-detection-using-cnn-techniques-research-open-access","Developing an efficient method for melanoma detection using CNN techniques - Research Open Access","","The study addresses the growing burden and high lethality of skin cancer, with melanoma identified as the clinically aggressive subtype responsible for most deaths. It outlines a deep-learning approach for early and accurate skin-cancer screening using MobileNetV2 and DenseNet-style architectures on the HAM10000 dataset of 10,000 skin lesion images. Model evaluation compares performance and integrates the best-trained model into a Python-based web interface for automated classification.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/developing-an-efficient-method-for-melanoma-detection-using-cnn-techniques-research-open-access/329937/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/developing-an-efficient-method-for-melanoma-detection-using-cnn-techniques-research-open-access/329937.png","ImageObject",300,407,{"name":42,"@type":43},"Ophelia","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-28","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What deep-learning models are used for melanoma detection in this research?","Question",{"text":63,"@type":64},"The research uses deep learning techniques including MobileNetV2 and a customized CNN (DenseNet-style) model for detecting malignant and benign tumors.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which dataset is used to train and evaluate the skin lesion detection models?",{"text":68,"@type":64},"The HAM10000 dataset is used, containing 10,000 skin lesion images covering both nonmelanocytic and melanocytic tumors.",{"name":70,"@type":61,"acceptedAnswer":71},"How accurate were the models after evaluation, and what application was built?",{"text":72,"@type":64},"MobileNetV2 achieved 85% accuracy, while the customized CNN achieved 95%. A Python (Flask) web application was developed to let users enter patient details, upload lesion images, and receive cancerous/non-cancerous predictions with a percentage display.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},329937,1790304809,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":112},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","Moturi et al.  \nJournal of the Egyptian National Cancer Institute (2024) 36:6  \n[https://doi.org/10.1186/s43046-024-00210-w](https://doi.org/10.1186/s43046-024-00210-w)  \nJournal of the Egyptian National Cancer Institute  \n RESEARCH Open Access  \nDeveloping an efficient method  \nfor melanoma detection using CNN techniques  \nDevika Moturi 1* , Ravi Kishan Surapaneni 1 and Venkata Sai Geethika Avanigadda1  \nAbstract  \nBackground More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically deadly. Any bodily part may become infected by cancerous cells, which can be fatal. Skin cancer is one of the most prevalent types of cancer, and its prevalence is rising across the globe. Squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and causes the majority of deaths, are the primary subtypes of skin cancer. Screening for skin cancer is therefore essential.  \nMethods The best way to quickly and precisely detect skin cancer is by using deep learning techniques. In this research deep learning techniques like MobileNetv2 and Dense net will be used for detecting or identifying two main kinds of tumors malignant and benign. For this research HAM10000 dataset is considered. This dataset consists of 10,000 skin lesion images and the disease comprises nonmelanocytic and melanocytic tumors. These two techniques can be used for detecting the malignant and benign. All these methods are compared and then a result can be inferred from their performance.  \nResults After the model evaluation, the accuracy for the MobileNetV2 was 85% and customized CNN was 95% . A web application has been developed with the Python framework that provides a graphical user inter  \nface with the best-trained model. The graphical user interface allows the user to enter the patient details and upload the lesion image. The image will be classified with the appropriate trained model which can predict whether the uploaded image is cancerous or non-cancerous. This web application also displays the percentage of cancer affected.  \nConclusion As per the comparisons between the two techniques customized CNN gives higher accuracy for the detection of melanoma.  \nKeywords Skin cancer detection, Melanoma, Deep learning, MobileNetV2, HAM10000, Customized CNN, Flask  \nBackground  \nCancer is the term for the uncontrollable growth of tissues in a particular bodily part [1]. Skin cancer seems tobe one of the dangerous diseases that is spreading vastly and quickly around the world. Skin cancer is a condition where uncontrollable growth of abnormal skin cells  \n*Correspondence:  \nDevika Moturi[devikamoturi@gmail.com](devikamoturi@gmail.com)  \n1 Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India  \noccurs [2]. Early detection and precise diagnosis are crucial for determining possible cancer therapies. Melanoma is very dangerous and also it is the deadliest form of skin cancer. Just 1% of all skin cancer cases come under the category of melanoma, but according to the statement given by the American Cancer Society melanoma will have a higher death rate [3]. The cells in which melanoma develops are known as melanocytes. When healthy melanocytes begin to multiply uncontrollably and create a malignant growth, the condition starts. It typically develops on sun-exposing regions of the body, like lips, hands, neck, and face. These types of cancers can only be treated if detected as early as possible they would propagate to  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are","cbCaidAehlRAsUMF","https://ap.wps.com/l/cbCaidAehlRAsUMF","pdf",1977432,12,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Background\n## Cancer and melanoma context\n# Methods and supporting discussion\n## Deep learning and CNN-based detection","[{\"question\":\"What deep-learning models are used for melanoma detection in this research?\",\"answer\":\"The research uses deep learning techniques including MobileNetV2 and a customized CNN (DenseNet-style) model for detecting malignant and benign tumors.\"},{\"question\":\"Which dataset is used to train and evaluate the skin lesion detection models?\",\"answer\":\"The HAM10000 dataset is used, containing 10,000 skin lesion images covering both nonmelanocytic and melanocytic tumors.\"},{\"question\":\"How accurate were the models after evaluation, and what application was built?\",\"answer\":\"MobileNetV2 achieved 85% accuracy, while the customized CNN achieved 95%. A Python (Flask) web application was developed to let users enter patient details, upload lesion images, and receive cancerous/non-cancerous predictions with a percentage display.\"}]","Developing an efficient method for melanoma detection using CNN techniques - Research Open Access | PDF",1789986215]