An Explainable SSL-Based Model for Robust Multi-Class Brain Tumor Classification from MRI Images

An Explainable SSL-Based Model for Robust Multi-Class Brain Tumor Classification from 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 Explainable SSL-Based Model for Robust Multi-Class Brain Tumor Classification from MRI Images Md.Faishal Ahmed Rudro, Shajedul Hasan Arman, Omar faruque siyam, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7725530/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 and interpretable brain tumor classification from magnetiSSc resonance imaging (MRI) is important for timely detection and effective treatment planning. Deep supervised learning methods, though strong, are limited by their reliance on vast labeled datasets and their lack of explainability in clinical decision-making. In this work, we introduce a self-supervised learning (SSL) approach based on SimCLR with EfficientNetB3 backbone for four-class brain tumor segmentation: glioma, meningioma, pituitary tumor, and no-tumor. The method employs SSL-based model pre-training on large amounts of unlabeled data to learn salient feature representations prior to performing supervised fine-tuning with an optimal classifier head. The technique effectively enhances generalization with minimal dependence on large-scale human annotation. The envisioned framework has a test accuracy of 98.32%, per-class precision, recall, and F1-measures over 96%, and best classification performance in no-tumor and pituitary classes. For improving interpretability and clinical confidence, Gradient-weighted Class Activation Mapping (Grad-CAM) was used with discriminative tumor region visualization and validation that model attention is in agreement with radiological features. To the best knowledge of the authors, it is the first work that combines an optimized SimCLR-based SSL with brain tumor classification using MRI and explainability. The results show that SSL-driven and interpretable models can have the capability of producing highly accurate, reliable, and clinically relevant decision support for neuro-oncology. Brain Tumor Classification Self-Supervised Learning SimCLR EfficientNet Medical Imaging Explainable AI Grad-CAM Clinical Decision Support 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-7725530","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525136014,"identity":"8a01a2fe-094c-4711-ba9a-65d9c9515e1b","order_by":0,"name":"Md.Faishal Ahmed Rudro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYFCCBIYPDAZAkh3I5mFgkAOJHXiAXwvjDLAWZogWY7CWBIJaGBBaEhsgVuMGuu0JjA0/Cury+Jt5n0m83WOTPj/s8EOgLXZyug3YtZidecDY2GNwuFjiMLuZ5Jxnabkbb6cZALUkG5sdwKHlRgL7Ax6DA4kNh9nYpHkOHM7dODsBpOVA4jbcWhgb/xjUJc6Hakk3nJ3+gaCWZh4D5sQNUC0J8tI5BGw587CxWcbgcOLGw2zMlnMOpBlukM4pOJBggMcvx5MPNr75U5c473gb4403B2zk5Wenb/7wocJODpcWBgbGBlS+AVilAS7l2IB8A0Elo2AUjIJRMMIAAIdUZaAP6LLtAAAAAElFTkSuQmCC","orcid":"","institution":"East West University","correspondingAuthor":true,"prefix":"","firstName":"Md.Faishal","middleName":"Ahmed","lastName":"Rudro","suffix":""},{"id":525136015,"identity":"66f38b26-e227-4ad5-8483-261f0f8819a8","order_by":1,"name":"Shajedul Hasan Arman","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Shajedul","middleName":"Hasan","lastName":"Arman","suffix":""},{"id":525136016,"identity":"1510395f-822b-4f55-b92a-f3da0068f3ed","order_by":2,"name":"Omar faruque siyam","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Omar","middleName":"faruque","lastName":"siyam","suffix":""},{"id":525136017,"identity":"e9f328a7-9120-4447-99a5-343109bd0bb6","order_by":3,"name":"Nur Nabi Rahman","email":"","orcid":"","institution":"Jagannath University","correspondingAuthor":false,"prefix":"","firstName":"Nur","middleName":"Nabi","lastName":"Rahman","suffix":""},{"id":525136018,"identity":"ba510ef5-01e8-4c18-8700-7149e5244fd3","order_by":4,"name":"Anzim Hasan Nabil","email":"","orcid":"","institution":"Govt. 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Images","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Brain Tumor Classification, Self-Supervised Learning, SimCLR, EfficientNet, Medical Imaging, Explainable AI, Grad-CAM, Clinical Decision Support","lastPublishedDoi":"10.21203/rs.3.rs-7725530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7725530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate and interpretable brain tumor classification from magnetiSSc resonance imaging (MRI) is important for timely detection and effective treatment planning. Deep supervised learning methods, though strong, are limited by their reliance on vast labeled datasets and their lack of explainability in clinical decision-making. In this work, we introduce a self-supervised learning (SSL) approach based on SimCLR with EfficientNetB3 backbone for four-class brain tumor segmentation: glioma, meningioma, pituitary tumor, and no-tumor. The method employs SSL-based model pre-training on large amounts of unlabeled data to learn salient feature representations prior to performing supervised fine-tuning with an optimal classifier head. The technique effectively enhances generalization with minimal dependence on large-scale human annotation. The envisioned framework has a test accuracy of 98.32%, per-class precision, recall, and F1-measures over 96%, and best classification performance in no-tumor and pituitary classes. For improving interpretability and clinical confidence, Gradient-weighted Class Activation Mapping (Grad-CAM) was used with discriminative tumor region visualization and validation that model attention is in agreement with radiological features. To the best knowledge of the authors, it is the first work that combines an optimized SimCLR-based SSL with brain tumor classification using MRI and explainability. The results show that SSL-driven and interpretable models can have the capability of producing highly accurate, reliable, and clinically relevant decision support for neuro-oncology.\u003c/p\u003e","manuscriptTitle":"An Explainable SSL-Based Model for Robust Multi-Class Brain Tumor Classification from MRI Images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 11:05:16","doi":"10.21203/rs.3.rs-7725530/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"adaf2cf4-093a-4083-8c37-faaec1024d03","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T15:55:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 11:05:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7725530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7725530","identity":"rs-7725530","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}