Melanoma Detection Using Deep Learning and Cool & Warm Color Segmentation

Melanoma Detection Using Deep Learning and Cool & Warm Color Segmentation | 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 Melanoma Detection Using Deep Learning and Cool & Warm Color Segmentation Zhiar Piroti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7329646/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 Melanoma is a highly aggressive form of skin cancer with a significantly improved prognosis when detected early. Traditional diagnostic methods often suffer from subjectivity and accessibility limitations, prompting the need for automated and accurate solutions. This study proposes a novel framework that integrates ResNet50 a deep convolutional neural network with a biologically inspired color segmentation technique that differentiates warm and cool color regions in dermoscopic images. The segmentation method filters out non-informative image areas, allowing the network to focus on clinically relevant zones associated with melanoma features. The model was trained and evaluated on the HAM10000 dataset, which includes over 10,000 dermoscopic images spanning seven lesion categories. Extensive data preprocessing, augmentation, and a 5-fold cross-validation strategy were applied to ensure generalizability. Quantitative results demonstrate that the proposed ResNet50+C&W model outperforms baseline models, achieving an accuracy of 90.3%, ROC-AUC of 88.0%, precision of 89.1%, recall of 88.2%, and F1-score of 88.8%. Threshold analysis identified 0.6 as the optimal decision boundary, balancing sensitivity (91.4%) and specificity (75.4%). These findings confirm that combining deep residual feature extraction with color-based lesion segmentation enhances diagnostic accuracy and interpretability. This framework offers a promising direction for scalable and clinically viable melanoma detection systems. Biomedical Engineering Deep Learning ResNet50 Color Segmentation Melanoma Classification Skin Lesion Analysis Full Text Additional Declarations The authors declare no competing interests. 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-7329646","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497848619,"identity":"0071a410-9413-4ff7-831c-97232d5f07b1","order_by":0,"name":"Zhiar Piroti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCcYGMM3GzHwAxJUhXgsfO1sCiMtDhBYoLcfPYwCiCWuRn93c+Lniz7Y8Nmaez69u1FjwMLAfProBnxbGOQebJc+23S5mY+bdZp1zDOgwnrS0G/i0MEskNkg2NtxObANqMc5hA2qR4DHDq4VNIrH5Z8MfkBaeZ8Y5/4jQwiOR2CbZwAbWwvw4t40ILRJALZaNYL+wmTHn9knwsBHyi/yM9Mc3gQ7Lk+8//Phzzrc6OX72w8fwaoGBBLC/wL4jRjlMC/MHYlWPglEwCkbByAIAhzdDcdtBqQQAAAAASUVORK5CYII=","orcid":"","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Zhiar","middleName":"","lastName":"Piroti","suffix":""}],"badges":[],"createdAt":"2025-08-08 18:31:03","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7329646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7329646/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88789649,"identity":"44c0a944-6984-4118-a232-d1daa0179013","added_by":"auto","created_at":"2025-08-11 12:32:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":978981,"visible":true,"origin":"","legend":"","description":"","filename":"14.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7329646/v1_covered_32e1a348-9f57-4ca7-8299-277fc01c5f38.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMelanoma Detection Using Deep Learning and Cool \u0026amp; Warm Color Segmentation\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Independent Researcher","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":"Deep Learning, ResNet50, Color Segmentation, Melanoma Classification, Skin Lesion Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7329646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7329646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMelanoma is a highly aggressive form of skin cancer with a significantly improved prognosis when detected early. 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