Hybrid Swin Transformer EfficientNet U-Net Model for Enhanced Brain Tumor Segmentation

Hybrid Swin Transformer EfficientNet U-Net Model for Enhanced Brain Tumor Segmentation | 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 Hybrid Swin Transformer EfficientNet U-Net Model for Enhanced Brain Tumor Segmentation Pankaj Kunekar, Aditya Yadav, Abhishek Yadav, Yash Dusankar, Yashraj Nalawade, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6964779/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 MRI is essential for diagnosis, treatment, and cognitive preservation during neurosurgical treatment. In this article, we present a novel hybrid deep learning architecture that synergistically integrates a Swin Transformer encoder, an EfficientNet3D lightweight feature extractor, and a U-Net inspired decoder. The model combines the global contextual representation ability of transformers with the efficacy and spatial information of convolutional networks. Our model was trained and evaluated on BraTS2020 dataset from using multimodal MRI inputs (T1, T1ce, T2, FLAIR) and achieved a mean Dice Similarity Coefficient (DSC) score of 0.7086, having subregion-wise scores of 0.8590 (Whole Tumor), 0.6551 (Enhancing Tumor), and 0.6117 (Tumor Core). The achievements outperform baseline CNN-based structures and demonstrate the superiority of our approach in depicting heterogeneous tumor structures. Segmentation output not only enhances radiological assessment but also enables surgical planning near functionally critical areas of the brain, reducing the risk of cognitive impairment. This two-stream hybrid network offers a very effective and robust solution for high-fidelity brain tumor segmentation, with strong potential for clinical adoption in neuro-oncologic workflows. Brain tumor segmentation MRI Deep learning Swin Transformer EfficientNet3D U-Net Dice Similarity Coefficient Hybrid neural architecture Neuro-oncology Cognitive preservation Medical image analysis Neurosurgical planning 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. 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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-6964779","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486855929,"identity":"a9dd1224-57b5-4169-8919-38ddeb33341a","order_by":0,"name":"Pankaj Kunekar","email":"","orcid":"","institution":"Vishwakarma Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Pankaj","middleName":"","lastName":"Kunekar","suffix":""},{"id":486855930,"identity":"099d90bf-0e3b-4fe7-bdbd-f540deecf90f","order_by":1,"name":"Aditya 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