Advancing Skin Cancer Detection: Harnessing the Power of CNNs

Advancing Skin Cancer Detection: Harnessing the Power of CNNs | 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 Advancing Skin Cancer Detection: Harnessing the Power of CNNs Dheiver Francisco Santos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4137983/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 The CNN model, based on YOLOv8.1.29, achieved an average classification accuracy of 53% across all classes. Higher confidence levels were observed in correctly classifying certain categories like akiec, bcc, bkl, and df, while lower confidence levels were noted for mel, nv, and vasc classes. The precision-confidence curve highlighted areas for improvement, with teachers generally exhibiting higher confidence levels compared to students. The Precision-Recall Curve provided a detailed assessment of the model's performance across different skin cancer classes. Visual examples demonstrated the model's ability to accurately identify lesions of various types. Overall, the CNN-based approach shows promise for early skin cancer diagnosis, with potential for further improvement through refinement and validation studies. Artificial Intelligence and Machine Learning Skin cancer detection Convolutional Neural Networks (CNNs) Dermatoscopic imagesm Deep learning Early diagnosis 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-4137983","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281881844,"identity":"b5bf8275-f6d5-4e34-973c-5f8bd6fa2427","order_by":0,"name":"Dheiver Francisco Santos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie2QMUvEMBiGvy5O8fZb7F9Il0yl90NcUgJ1qTq4OHTIUeiNN+dHuDqnBOpS7RqoyB2uN8S9iLFQ8NC2uAnmgZAv8D68fAFwOP4iGkB+eYb2eGv5c/a74nGApL9nlYFPRfXTpLJo81Kh2xfwN48PO9M10d1G2ZYsPB9Tls8VVai+AVxfxWtRtOy+jq1SJZd8RME6xaUoKGBIg/yUt4xIq3hcTSjXphTvFPztIchR98RIs59TUpBvnNp/sC3oREZEz7QsdYKlqSjC+hAIUTBKtG2hE7ssNHs1NKNn/jbFxnTRijQX+53JwlFlAA1D3CfpTPyI1W/CDofD8T/4AAC+bDkZcgK2AAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Dheiver","middleName":"Francisco","lastName":"Santos","suffix":""}],"badges":[],"createdAt":"2024-03-20 14:46:38","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4137983/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4137983/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53182238,"identity":"d82768c0-28b7-4f48-9d08-3efbb460260d","added_by":"auto","created_at":"2024-03-21 15:47:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2221212,"visible":true,"origin":"","legend":"","description":"","filename":"AdvancingSkinCancerDetectionHarnessingthePowerofCNNs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4137983/v1_covered_b6d36536-cb58-47f9-8554-49f19caaa933.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAdvancing Skin Cancer Detection: Harnessing the Power of CNNs\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"CATI - Advanced Center for Intelligent Technologies","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":"Skin cancer detection, Convolutional Neural Networks (CNNs), Dermatoscopic imagesm, Deep learning, Early diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-4137983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4137983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe CNN model, based on YOLOv8.1.29, achieved an average classification accuracy of 53% across all classes. Higher confidence levels were observed in correctly classifying certain categories like akiec, bcc, bkl, and df, while lower confidence levels were noted for mel, nv, and vasc classes. The precision-confidence curve highlighted areas for improvement, with teachers generally exhibiting higher confidence levels compared to students. The Precision-Recall Curve provided a detailed assessment of the model's performance across different skin cancer classes. Visual examples demonstrated the model's ability to accurately identify lesions of various types. Overall, the CNN-based approach shows promise for early skin cancer diagnosis, with potential for further improvement through refinement and validation studies.\u003c/p\u003e","manuscriptTitle":"Advancing Skin Cancer Detection: Harnessing the Power of CNNs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 15:39:28","doi":"10.21203/rs.3.rs-4137983/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":"a6c35f6c-abbf-47bb-a0a7-2bd3953d1bb8","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29681844,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2024-03-21T15:39:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 15:39:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4137983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4137983","identity":"rs-4137983","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}