Skin cancer diagnosis (SCD) using EfficientNet-Wavelet and Gray Wolf Optimization (GWO)

Skin cancer diagnosis (SCD) using EfficientNet-Wavelet and Gray Wolf Optimization (GWO) | 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 Skin cancer diagnosis (SCD) using EfficientNet-Wavelet and Gray Wolf Optimization (GWO) Ramin Mousa, Amir Ali Bengari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5976470/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 One of the most dangerous types of cancer is skin cancer (SC), which is seen in the form of skin lesions in the patient and can threaten the patient's life if not treated on time. With early diagnosis of this disease, more effective treatment methods can be used and the progression of the disease can be prevented. Various machine learning and deep learning methods have been developed for early skin cancer diagnosis. However, one of the benefits of deep learning is the ability to learn enormous volumes of data which abundances the trend towards deep learning methods. In this article, a method based on the combination of EfficientNet and Wavelet is presented to classify skin cancer images. Various combinations of the EfficientNet approach, including B0, B1, B2 and B3, are considered for this purpose. Also, Gray Wolf Optimization (WGO) is used to find the optimal values of the features. Two datasets of ISIC-2016 and ISIC-2017 are considered for model evaluation. Based on the obtain results, the EfficientNetB3+Wavelet+GWO model obtained the best result on the ISIC-2016 data and is able to achieve an accuracy of 0.9814 and an F-measure of 0.9827. Furthermore, in the ISIC-2017 data set, the EfficientNetB1+Wavelet+GWO model achieves the best performance with an accuracy of 0.9795 and an F-measure of 0.9797 in ISIC-2017 data. Skin cancer diagnosis (SCD) Gray Wolf Optimization (WGO) Transfer learning Wavelet transform EfficientNet 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. 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-5976470","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414114121,"identity":"3aaa0694-ab95-44c5-8153-8a2b44763e2f","order_by":0,"name":"Ramin Mousa","email":"","orcid":"","institution":"University of Zanjan","correspondingAuthor":false,"prefix":"","firstName":"Ramin","middleName":"","lastName":"Mousa","suffix":""},{"id":414114122,"identity":"8c6a2c76-e46f-45f8-9d97-44ba34c1a236","order_by":1,"name":"Amir Ali Bengari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYFACxgYGhgIJBgMIT0IOKixBQIsBQosxAxtBLSBgwADTwpDYwEZAMf+0w20ffhhYMJiz9x5+8XOPRfqG+w2MH34wWOTj0iJxO7F5Zg/QYZY959Ise55J5G44xsAs2cMgYdmASw9QCwMPyC83cswMeA6AtTBIA80ywKVDHqiF8Q9Iy/03ZoZ/DkikGwBt+Y1PiwFQCzPEFh7jx0BbEoBa2PDaYgjSImMgwWPZk2PGLHNAwnDmscQ2S6DvcGqRu53+mPFNRZ2cOfsZ449vDtTJ8x0+fPjGj4o6nFpggAeI2aDRB45cQhoggPkDcepGwSgYBaNgpAEAQMhMpQHh6mgAAAAASUVORK5CYII=","orcid":"","institution":"University of Tehran","correspondingAuthor":true,"prefix":"","firstName":"Amir","middleName":"Ali","lastName":"Bengari","suffix":""}],"badges":[],"createdAt":"2025-02-06 22:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5976470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5976470/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76379853,"identity":"3d917251-93f1-4937-800b-76cd0dc2409d","added_by":"auto","created_at":"2025-02-16 04:08:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":435685,"visible":true,"origin":"","legend":"","description":"","filename":"SkincancerdiagnosisusingEfficientNetWaveletandGrayWolfOptimization.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5976470/v1_covered_1d3e4574-5137-4ec7-bba5-52f4ec3159c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Skin cancer diagnosis (SCD) using EfficientNet-Wavelet and Gray Wolf Optimization (GWO)","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":"Skin cancer diagnosis (SCD), Gray Wolf Optimization (WGO), Transfer learning, Wavelet transform, EfficientNet","lastPublishedDoi":"10.21203/rs.3.rs-5976470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5976470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"One of the most dangerous types of cancer is skin cancer (SC), which is seen in the form of skin lesions in the patient and can threaten the patient's life if not treated on time. With early diagnosis of this disease, more effective treatment methods can be used and the progression of the disease can be prevented. Various machine learning and deep learning methods have been developed for early skin cancer diagnosis. However, one of the benefits of deep learning is the ability to learn enormous volumes of data which abundances the trend towards deep learning methods. In this article, a method based on the combination of EfficientNet and Wavelet is presented to classify skin cancer images. Various combinations of the EfficientNet approach, including B0, B1, B2 and B3, are considered for this purpose. Also, Gray Wolf Optimization (WGO) is used to find the optimal values of the features. Two datasets of ISIC-2016 and ISIC-2017 are considered for model evaluation. Based on the obtain results, the EfficientNetB3+Wavelet+GWO model obtained the best result on the ISIC-2016 data and is able to achieve an accuracy of 0.9814 and an F-measure of 0.9827. Furthermore, in the ISIC-2017 data set, the EfficientNetB1+Wavelet+GWO model achieves the best performance with an accuracy of 0.9795 and an F-measure of 0.9797 in ISIC-2017 data.","manuscriptTitle":"Skin cancer diagnosis (SCD) using EfficientNet-Wavelet and Gray Wolf Optimization (GWO)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-13 04:36:35","doi":"10.21203/rs.3.rs-5976470/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":"43eb26af-7aa9-445b-97f2-4f2d5d5902f8","owner":[],"postedDate":"February 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-21T09:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-13 04:36:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5976470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5976470","identity":"rs-5976470","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}