Enhancing Brain Tumor Segmentation with Transformer-Based Models: A Study on the BraTS 2020 Dataset

Enhancing Brain Tumor Segmentation with Transformer-Based Models: A Study on the BraTS 2020 Dataset | 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 Article Enhancing Brain Tumor Segmentation with Transformer-Based Models: A Study on the BraTS 2020 Dataset Isaac Osei, James Ben Hayfron-Acquah, Michael Asante, Benjamin Appiah, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5912374/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 Accurate segmentation of brain tumors from medical images is crucial for clin- ical diagnosis, treatment planning, and patient outcome prediction. While the UNet architecture has long been a standard in biomedical image segmentation, recent advancements in transformer-based models have opened new possibilities for improving segmentation performance. This study conducts a comparative analysis of three models for brain tumor segmentation: Standard 3D UNet, Swin Transformer-Enhanced 3D UNet, and Detection Transformer-Enhanced 3D UNet. The BraTS 2020 dataset is utilized to train and evaluate these mod- els. Experimental results reveal that the Detection Transformer-Enhanced UNet achieves the highest segmentation accuracy, showcasing its ability to effectively capture long-range dependencies and complex features, surpassing both the stan- dard UNet and the Swin Transformer-Enhanced UNet. These findings highlight the potential of integrating transformer architectures to advance the state of medical image segmentation. Biological sciences/Biotechnology Biological sciences/Cancer Swin Transformer Detection Transformer 3D UNet 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-5912374","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":415246596,"identity":"e42f6ecb-4c24-41ff-8c24-099cbeb3c8bb","order_by":0,"name":"Isaac Osei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYJCCAwkgkr2BgZlELTwHSNACARIJRGrh5z988MCDP3by/DPfGH4uqLBh4G/vTsCrRXJGWsKBxLZkwxm3c4ylZ5xJY5A4c3YDXi0GN3gMDiQ2MDNukM4xkOZtO8xgIJFLQMv58x8OJPypt98gecb4N3FaDuQAQ4ztcOIGCR4z4mwB+gXosLbjyUBvlFnznEnjIegXYIg9/vjjT7Vtf/vhzbd5Kmzk+Nt78WtBAhwGIJKHWOUgwP6AFNWjYBSMglEwggAAAiVIu1K1y0IAAAAASUVORK5CYII=","orcid":"","institution":"Ho Technical University","correspondingAuthor":true,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Osei","suffix":""},{"id":415246599,"identity":"6c3ebd42-6a4e-4793-bee6-220236c917fa","order_by":1,"name":"James Ben Hayfron-Acquah","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"Ben","lastName":"Hayfron-Acquah","suffix":""},{"id":415246601,"identity":"90e7faee-664d-4186-91ca-3c85362c6f92","order_by":2,"name":"Michael Asante","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Asante","suffix":""},{"id":415246603,"identity":"7f3fa125-cce8-43c8-a5b0-73508c48eb17","order_by":3,"name":"Benjamin Appiah","email":"","orcid":"","institution":"Ho Technical University","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Appiah","suffix":""},{"id":415246605,"identity":"7e6128c3-a46b-4ae1-b109-316b0f2a7705","order_by":4,"name":"Kwabena Owusu-Agyemang","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Kwabena","middleName":"","lastName":"Owusu-Agyemang","suffix":""}],"badges":[],"createdAt":"2025-01-27 12:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5912374/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5912374/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80891922,"identity":"4177e7be-695d-479a-964b-bdced4c8dbf6","added_by":"auto","created_at":"2025-04-18 10:31:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":706535,"visible":true,"origin":"","legend":"","description":"","filename":"transformer16.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5912374/v1_covered_5d0a56db-abf2-4db7-b903-1e4f8d24d6fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Brain Tumor Segmentation with Transformer-Based Models: A Study on the BraTS 2020 Dataset","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":"Swin Transformer, Detection Transformer,3D UNet","lastPublishedDoi":"10.21203/rs.3.rs-5912374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5912374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate segmentation of brain tumors from medical images is crucial for clin- ical diagnosis, treatment planning, and patient outcome prediction. 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