A Melanoma Diagnosis Using Dermoscopy Images Utilizing Convolutional Neural Networks

A Melanoma Diagnosis Using Dermoscopy Images Utilizing Convolutional Neural Networks | 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 A Melanoma Diagnosis Using Dermoscopy Images Utilizing Convolutional Neural Networks Archishma Marrapu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3945997/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 type of skin cancer, the most dangerous one with over 200,000 new cases in the United States annually. Background: Melanoma can affect a wide range of people and can be affected by different risk factors, although the exact cause is unknown. There are several diagnosis methods currently used by doctors in a traditional method of diagnosis. These methods of diagnosis come with different disadvantages and drawbacks, therefore, decreasing the accuracy and effectiveness of the diagnosis. The drawbacks that occur with the traditional, manual diagnosis are misdiagnosis of melanoma, low accuracy in diagnosis, the abundance of time, and human errors. The automation of melanoma diagnosis can benefit many people, both doctors, and patients, in several different ways. Methods: The usage of new technologies, such as Artificial Intelligence, can increase the effectiveness and accuracy of diagnosis. This paper proposes an Artificial Intelligence and Deep Learning model that diagnoses melanoma with more accuracy and less time at an early stage. It is essentially a mobile application that can be integrated with a handheld dermatoscope to capture dermoscopy images to diagnose melanoma using the pre-trained Machine Learning model. The pre-trained model uses several images in order to produce accurate and effective results. Results: The model has a 99% accuracy rate and can produce results of the diagnosis within a few seconds of time. Conclusions: This method of diagnosis can eliminate manual errors in diagnosis and can also reduce the several weeks of wait time needed for manual melanoma diagnosis results. Full Text Additional Declarations No competing interests reported. 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. 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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-3945997","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278679389,"identity":"228f154a-0c37-41e9-918d-e0281c9c16fc","order_by":0,"name":"Archishma Marrapu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYHACNoYEGPMDiMtOihbGGSAuMzFaYICZB0wSUM8/+/CzBw9z7PLN288ek7b5tU2ej5mB8cPHHNxaJM6lmRskbku2nHMmL006t++2YRszA7PkzG14rDnDwyaRuI3ZQIIhx0w6t+c2I1ALGzMvHi3yEC31BhL8b8ykLXtu2xPUYgDRcthAQgJoC8OP24kEtRieYTMDajkO1PIu2bK34XZyGzNjM16/yJ1hfib5c1s10GG5B2/8+HPbdn5788EPH/F5HwGAkcLYBmIwNhClHqKF4Q+xikfBKBgFo2AkAQAiaUgMbuGnQAAAAABJRU5ErkJggg==","orcid":"","institution":"Thomas Jefferson High School for Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Archishma","middleName":"","lastName":"Marrapu","suffix":""}],"badges":[],"createdAt":"2024-02-10 13:44:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3945997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3945997/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58622004,"identity":"6657998f-793f-44a1-85cd-ee8071a36c9e","added_by":"auto","created_at":"2024-06-19 03:32:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":642936,"visible":true,"origin":"","legend":"","description":"","filename":"AMelanomaDiagnosisUsingDermoscopyImagesUtilizingConvolutionalNeuralNetworks.docx.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3945997/v1_covered_d4e52595-c349-4df6-8a93-7790dec63517.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Melanoma Diagnosis Using Dermoscopy Images Utilizing Convolutional Neural Networks","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":"","lastPublishedDoi":"10.21203/rs.3.rs-3945997/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3945997/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMelanoma is a type of skin cancer, the most dangerous one with over 200,000 new cases in the United States annually.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Melanoma can affect a wide range of people and can be affected by different risk factors, although the exact cause is unknown. 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