An Efficient Deep Learning Technique for Brain Abnormality Detection Using MRI Images

An Efficient Deep Learning Technique for Brain Abnormality Detection Using MRI Images | 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 An Efficient Deep Learning Technique for Brain Abnormality Detection Using MRI Images Shilpa Mahajan, Anuradha Dhull, Aryan Dahiya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3828732/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 This study focuses on leveraging advanced medical imaging techniques, encompassing X-rays and MRIs, to effectively detect brain anomalies, notably tumors. The conventional manual examination approach is time-intensive and often suboptimal. The study proposes a novel method employing machine learning algorithms to categorize 700 patient images as either "brain" or "non-brain" following meticulous labelling and preprocessing. The binary classification comprises "Normal" and "Abnormal" classes, with model accuracy refined through adjustments and augmented training on expanded datasets. Through comprehensive model evaluation including ANN, CNN, VGG-16, and AlexNet, the VGG-16-based model emerges with the highest accuracy at 94.4%. This research underscores the immense potential of advanced deep learning, ensuring swift and precise brain abnormality detection in medical imaging with significant clinical implications. MRI Deep Learning VGG 16 CNN Brain Abnormalities Classification 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-3828732","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264786038,"identity":"20dbb2fc-051e-4f58-bb29-a475af98a339","order_by":0,"name":"Shilpa Mahajan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACCcbGAwwMNkAWYwMDjwFIKIGglgagljSStDAwALUchnB4GIjQIj+7ueFwQc35PP4ZyW0P3hTYMfCz5xjg1WJw52DD4RnHbhdL3EhsN5xjkMwg2fOGgBaJxIbDPGy3EzdIJLZJ8xgwMxjcIGCL/AyQln/nYFrqGewJaWG4AdTC23YApuUw0F5CfgFpmdmXnDjjzMM2yTkGx3kkzjwrIOCw9IePC77ZJfa3pz+TePOnWo6/PXkDfocBATMyh4egcgwto2AUjIJRMAowAADr1EozJthw2gAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Shilpa","middleName":"","lastName":"Mahajan","suffix":""},{"id":264786039,"identity":"9ff8e358-6b11-4b94-ba55-550b2f6a6401","order_by":1,"name":"Anuradha Dhull","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anuradha","middleName":"","lastName":"Dhull","suffix":""},{"id":264786040,"identity":"7e9a031d-e01e-410d-92c1-50c722604909","order_by":2,"name":"Aryan Dahiya","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Aryan","middleName":"","lastName":"Dahiya","suffix":""}],"badges":[],"createdAt":"2024-01-02 06:31:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3828732/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3828732/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49919721,"identity":"272973ef-00b9-4a68-86cd-e23a7d2ada19","added_by":"auto","created_at":"2024-01-20 23:22:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1001970,"visible":true,"origin":"","legend":"","description":"","filename":"BrainAryan.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3828732/v1_covered_3041b80d-cb82-4b18-954f-04bc1b1e5ac7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Efficient Deep Learning Technique for Brain Abnormality Detection Using MRI Images","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":"MRI, Deep Learning, VGG 16, CNN, Brain, Abnormalities, Classification","lastPublishedDoi":"10.21203/rs.3.rs-3828732/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3828732/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study focuses on leveraging advanced medical imaging techniques, encompassing X-rays and MRIs, to effectively detect brain anomalies, notably tumors. 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