Helping Medical Doctors Diagnose Cervical Cancer Using Decision Making based Deep Learning System

Helping Medical Doctors Diagnose Cervical Cancer Using Decision Making based Deep Learning System | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Helping Medical Doctors Diagnose Cervical Cancer Using Decision Making based Deep Learning System Essam Alshareef , Sedigh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3996243/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 Cervical cancer is a leading cancer in the female population. This disease is considered dangerous as its slow and unpredicted growth. The prevention of such cancer can be mostly achieved by screening its transformation zones. The cervical pre-cancerous zones can be considered as three types: type 1, type 2, and type 3. Screening and analyzing these three stages can be crucial for preventing their transformation into cancer. Hence, it is essentially important to have an automated and intelligent system that can grade the cervical pre-cancerous colposcopy images into one of the three types. This can help in providing the right treatment and prevent cancer transformation. In this paper, we develop a residual learning-based model (ResNet-18) to be trained for classifying the type of a colposcopy cervical image into type 1, type 2, and type 3. Experimentally, the model was fine-tuned and evaluated on a public dataset of colposcopy cervical images and achieved promising results in cervical cancer screening of accuracy of 77% and F1-score of 79%. Bioinformatics Cervical cancer colposcopy ResNet-50 Residual learning 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 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-3996243","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":275350525,"identity":"9c89fb42-b618-414d-9bd3-3bbfa900f41b","order_by":0,"name":"Essam Alshareef , Sedigh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3PsQrCMBCA4QuBcwl2bWnFVygU6iDoq1i6Bh1cFKEODo6ufZLMQsAugmuhi7VPoCAoLkZBcGroJph/CBnuIxcAk+mHGwGQ8qgurN2A0MB/EWxCQvt10xLLyspLDsm4t17h/MoHHgItT3kNcVIeuBzk1NtLLDoiVothEPAa4ucMFNlGqT3ZFY6gijB068jwkFUPDkmUdiucOmKpJz6MQjVA1SsUyVlIPbFzHva5L6OUxdQlImNINX+xNllV8JlarCXJ+S4WQ6u1Kqs68lnvHWXvUzv+Fbk1mTaZTKa/6QkH9EBn8D6WqAAAAABJRU5ErkJggg==","orcid":"","institution":"Biotechnology Research Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sedigh","middleName":"Essam","lastName":"Alshareef","suffix":""}],"badges":[],"createdAt":"2024-02-28 09:24:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-3996243/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3996243/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51784718,"identity":"90c5b4d2-b272-4be7-b6aa-8977beada7a8","added_by":"auto","created_at":"2024-02-29 02:48:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":503091,"visible":true,"origin":"","legend":"","description":"","filename":"esamp2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996243/v1_covered_b996f207-7615-4fbf-9cc5-42e086d92fdb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eHelping Medical Doctors Diagnose Cervical Cancer Using Decision Making based Deep Learning System\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"biotechnology research center, Libya","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":"Cervical cancer, colposcopy, ResNet-50, Residual learning","lastPublishedDoi":"10.21203/rs.3.rs-3996243/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3996243/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCervical cancer is a leading cancer in the female population. This disease is considered dangerous as its slow and unpredicted growth. The prevention of such cancer can be mostly achieved by screening its transformation zones. The cervical pre-cancerous zones can be considered as three types: type 1, type 2, and type 3. Screening and analyzing these three stages can be crucial for preventing their transformation into cancer. Hence, it is essentially important to have an automated and intelligent system that can grade the cervical pre-cancerous colposcopy images into one of the three types. This can help in providing the right treatment and prevent cancer transformation. \u0026nbsp;In this paper, we develop a residual learning-based model (ResNet-18) to be trained for classifying the type of a colposcopy cervical image into type 1, type 2, and type 3. 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