BrainMNet: A Unified Neural Network Architecture for Brain Image Classification

BrainMNet: A Unified Neural Network Architecture for Brain Image Classification | 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 BrainMNet: A Unified Neural Network Architecture for Brain Image Classification Sudip Ghosh, Deepti _, Shivam Gupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3319476/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract In brain-related diseases, including Brain Tumours and Alzheimer's, accurate and timely diagnosis is crucial for effective medical intervention. Current state-of-the-art (SOTA) approaches in medical imaging predominantly focus on diagnosing a single brain disease at a time. However, recent research has uncovered intricate connections between various brain diseases, realizing that treating one condition may lead to the development of others. Consequently, there is a growing need for accurate diagnostic systems addressing multiple brain-related diseases. Designing separate models for different diseases, however, can impose substantial overhead. To tackle this challenge, our paper introduces BrainMNet, an innovative neural network architecture explicitly tailored for classifying brain images. The primary objective is to propose a single, robust framework capable of diagnosing a spectrum of brain-related diseases. The paper comprehensively validates BrainMNet's efficacy, specifically in diagnosing Brain tumours and Alzheimer's disease. Remarkably, the proposed model workflow surpasses current SOTA methods, demonstrating a substantial enhancement in accuracy and precision. Furthermore, it maintains a balanced performance across different classes in the Brain tumour and Alzheimer's dataset, emphasizing the versatility of our architecture for precise disease diagnosis. BrainMNet undergoes an ablation study to optimize its choice of the optimal optimizer, and a data growth analysis verifies its performance on small datasets, simulating real-life scenarios where data progressively increases over time. Thus, this paper signifies a significant stride toward a unified solution for diagnosing diverse brain-related diseases. Alzheimer Brain tumour Deep Learning Multiple Disease Diagnosis Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-3319476","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":231461719,"identity":"698ea6fa-bccd-4cd4-b6a2-381c131b6cc7","order_by":0,"name":"Sudip Ghosh","email":"","orcid":"","institution":"Dr. B.C. 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Current state-of-the-art (SOTA) approaches in medical imaging predominantly focus on diagnosing a single brain disease at a time. However, recent research has uncovered intricate connections between various brain diseases, realizing that treating one condition may lead to the development of others. Consequently, there is a growing need for accurate diagnostic systems addressing multiple brain-related diseases. Designing separate models for different diseases, however, can impose substantial overhead. To tackle this challenge, our paper introduces BrainMNet, an innovative neural network architecture explicitly tailored for classifying brain images. The primary objective is to propose a single, robust framework capable of diagnosing a spectrum of brain-related diseases. The paper comprehensively validates BrainMNet's efficacy, specifically in diagnosing Brain tumours and Alzheimer's disease. 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