Advanced Brain Tumor Classification Utilizing MaskR-CNN with Fuzzy C-Means Clustering

Advanced Brain Tumor Classification Utilizing MaskR-CNN with Fuzzy C-Means Clustering | 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 Advanced Brain Tumor Classification Utilizing MaskR-CNN with Fuzzy C-Means Clustering Muthulakshmi K, Jayalakshmi M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4659662/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 Brain tumors are a major concern in medical diagnostics, necessitating precise and prompt detectionfor optimal treatment. While radiologists may diagnose them using medical imaging, automating theprocess has several advantages, including increased efficiency and accessibility. Magnetic resonanceimaging (MRI) is the most effective way to detect these tumors. An automated system that accuratelyidentifies and localizes them in MRI scans can greatly benefit patients lacking immediate access toa radiologist. Our study proposes a method combining Mask R-CNN and Fuzzy C-Means (FCM)clustering to boost the accuracy of brain tumor detection in MRI images. This approach identifiestumors and segments abnormal brain tissues but also gauges the likelihood of tumor presence, offering acomprehensive diagnostic tool. While Mask R-CNN excels in segmentation accuracy, integrating FCMclustering further refines the detection process. FCM, which allows data to belong to multiple clusters,is proven in medical images where tumor boundaries can be ambiguous. Our experiment utilizes theBraTS 2018 and 2020 datasets, employing five-fold cross-validation to demonstrate the effectiveness andvalidate the clinical application of our proposed method. FCM Mask R-CNN MRI segmentation Tumor detection 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-4659662","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323861917,"identity":"9b298ff9-c65b-4498-bb15-de76478cd014","order_by":0,"name":"Muthulakshmi K","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Muthulakshmi","middleName":"","lastName":"K","suffix":""},{"id":323861918,"identity":"dba4ec9d-7414-4e5f-907f-bf9ed847b5e1","order_by":1,"name":"Jayalakshmi M","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHCCBCC2YJBg4AEyK0AkCBgQ1CIB1XKGgYeHCC0MCC2MbQwwa3AD3QaGh595aiTkJfvPHt3wcN5hGXsG5ocfGAru4NRidoAhWZrnmIThbIm8tBuJ2w4DHcZmLMFg8AyflgRpHjYJxnkSPGZALWkgv5gB/XIYry2/ef5J2M/jPwPUMgekhf0bIS1p0rxtEomzGXKAWhpsgFp4CNhymCHNcm6fRPLMGUAtCceAWg7zFEsk4NNyvCf5xptvNrYzzp8xu/mjRsKevb1944cPf3BrYWDmSWBCjQpmBkj04gbsBxh/4FUwCkbBKBgFIx4AAEqTS/LToWW+AAAAAElFTkSuQmCC","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":true,"prefix":"","firstName":"Jayalakshmi","middleName":"","lastName":"M","suffix":""}],"badges":[],"createdAt":"2024-06-29 14:10:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4659662/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4659662/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60946193,"identity":"04216017-d327-41ad-836f-73b4804436c8","added_by":"auto","created_at":"2024-07-23 23:00:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1090161,"visible":true,"origin":"","legend":"","description":"","filename":"document.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4659662/v1_covered_1a6bc6a2-d996-4036-bdf1-f39212a2826d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advanced Brain Tumor Classification Utilizing MaskR-CNN with Fuzzy C-Means Clustering","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":"FCM, Mask R-CNN, MRI segmentation, Tumor detection","lastPublishedDoi":"10.21203/rs.3.rs-4659662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4659662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Brain tumors are a major concern in medical diagnostics, necessitating precise and prompt detectionfor optimal treatment. 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