Proposed Visual Explainable model in Melanoma Detection and Risk Prediction using Modified ResNet50

Proposed Visual Explainable model in Melanoma Detection and Risk Prediction using Modified ResNet50 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Proposed Visual Explainable model in Melanoma Detection and Risk Prediction using Modified ResNet50 Sarvachan Verma, Ajitesh Kumar, Manoj Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5785966/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study proposed an enhanced visual explainable model for melanoma detection and risk prediction. We utilized the HAM10000 dataset, applying pre-processing techniques to improve image quality. Feature extraction and segmentation were performed using a U-Net model-based Dual Stream CNN-Transformer technique. Feature selection was optimized using the Henry Gas Solubility Optimization (HGSO) algorithm and the Water Strider Algorithm (WSA). A Deep Learning Model (DLM), specifically the Optimal Multi-Attention Fusion (MAF) ConvNeXt, was trained for melanoma detection. For disease severity prediction, we introduced a Modified ResNet-50 model combined with the Explainable AI technique Grad-CAM, providing visual explanations for the model's predictions. Experimental results demonstrate a robust classification performance with an AUC of 0.997, recall of 99%, and precision of 99.5%. This study aims to diagnose an accurate, efficient, melanoma and risk assessment. The Algorithm source code can be accessed at https://github.com/SarvachanVerma/Visual-Explanible-code-for-Melanoma_Matlab Melanoma Skin Cancer Detection Residual Neural Network Explainable AI Disease Severity Prediction and Dermoscopic Image Analysis Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5785966","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406863519,"identity":"d44bdd4e-a535-41e7-a41d-e82741bc7735","order_by":0,"name":"Sarvachan Verma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYPCCA3CWHJj7gBQtxmBuAilaEhtAJD4t8u1nn0n8qLkjb87AY/i4MscufX7Y4YdAW+zkdBuwazE4k24m2XPsmeHOBh5jw7PbknM33k4zAGpJNjY7gEMLQxqzAW/DYcYNB9jSJBu3MedunJ0A0nIgcRsOLfL9z5gN/zYctgdqSf/ZuK0+3XB2+ge8WhhupDE+BtqSuOEA8zHGxm2HE+Slc/DbYnDjGeNjmWOHkzccZj4MdNhxww3SOQUHEgxw+0W+P43h4Juaw7Ybjjc2fmzcVi0vPzt984cPFXZyuLQgADPM3gOQYCEByDeQonoUjIJRMApGAgAAyR5l4rYZCVAAAAAASUVORK5CYII=","orcid":"","institution":"GLA University","correspondingAuthor":true,"prefix":"","firstName":"Sarvachan","middleName":"","lastName":"Verma","suffix":""},{"id":406863520,"identity":"7f4971c0-7ce9-4009-a976-f7495c208418","order_by":1,"name":"Ajitesh Kumar","email":"","orcid":"","institution":"GLA University","correspondingAuthor":false,"prefix":"","firstName":"Ajitesh","middleName":"","lastName":"Kumar","suffix":""},{"id":406863524,"identity":"28b01947-8be2-43e7-9491-33111e38e13d","order_by":2,"name":"Manoj Kumar","email":"","orcid":"","institution":"Guru Ghasidas University: Guru Ghasidas Vishwavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Manoj","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-01-08 05:57:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5785966/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5785966/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78780595,"identity":"8d521f3d-916b-4a68-b9d4-e9a17a62e236","added_by":"auto","created_at":"2025-03-18 20:13:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":835364,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5785966/v1_covered_17a901e4-c36d-4038-a513-f07c2cff0b1b.pdf"}],"financialInterests":"","formattedTitle":"Proposed Visual Explainable model in Melanoma Detection and Risk Prediction using Modified ResNet50","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Melanoma, Skin Cancer Detection, Residual Neural Network, Explainable AI, Disease Severity Prediction and Dermoscopic Image Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5785966/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5785966/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposed an enhanced visual explainable model for melanoma detection and risk prediction. We utilized the HAM10000 dataset, applying pre-processing techniques to improve image quality. Feature extraction and segmentation were performed using a U-Net model-based Dual Stream CNN-Transformer technique. Feature selection was optimized using the Henry Gas Solubility Optimization (HGSO) algorithm and the Water Strider Algorithm (WSA). A Deep Learning Model (DLM), specifically the Optimal Multi-Attention Fusion (MAF) ConvNeXt, was trained for melanoma detection. For disease severity prediction, we introduced a Modified ResNet-50 model combined with the Explainable AI technique Grad-CAM, providing visual explanations for the model's predictions. Experimental results demonstrate a robust classification performance with an AUC of 0.997, recall of 99%, and precision of 99.5%. This study aims to diagnose an accurate, efficient, melanoma and risk assessment. The Algorithm source code can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/SarvachanVerma/Visual-Explanible-code-for-Melanoma_Matlab\u003c/span\u003e\u003cspan address=\"https://github.com/SarvachanVerma/Visual-Explanible-code-for-Melanoma_Matlab\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e","manuscriptTitle":"Proposed Visual Explainable model in Melanoma Detection and Risk Prediction using Modified ResNet50","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 10:02:48","doi":"10.21203/rs.3.rs-5785966/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c554e884-5356-46c6-97cc-aa1b9a500e47","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-18T20:05:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-28 10:02:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5785966","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5785966","identity":"rs-5785966","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}