{"paper_id":"4e468f17-caf8-4641-9f85-45cbd8313f6c","body_text":"Multimodal deep learning framework for Alzheimer’s stage classification using GAN-augmented MRI and clinical data | 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 Multimodal deep learning framework for Alzheimer’s stage classification using GAN-augmented MRI and clinical data Talha Shahan Ahmad, Ghulam Hussain Noori, Abdul Wahab, Yin Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7785803/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 Alzheimer’s disease (AD) diagnosis remains challenging due to the scarcity of annotated medical imaging data, pronounced class imbalance, and the subtlety of early-stage structural brain changes. Traditional deep learning approaches often overfit to majority classes and lack interpretability, limiting their clinical adoption. We propose a multimodal deep learning framework that fuses MRI-derived spatial features with standardized clinical parameters to enhance Alzheimer’s stage classification. The pipeline incorporates a GAN-based augmentation module to synthetically expand minority class samples, validated using MS-SSIM and expert review, thereby mitigating class imbalance. MRI features are extracted using a fine-tuned ResNet50, where lower convolutional layers preserve general spatial feature detectors while higher layers adapt to AD-specific morphological patterns. Clinical features, normalized via z-score scaling, are concatenated with MRI-derived embeddings to form a unified multimodal representation. This joint vector is processed through fully connected layers for four-stage classification Non-Demented, Very Mild Demented, Mild Demented and Moderate Demented. Model interpretability is ensured via Grad-CAM for spatial saliency mapping and clinical feature attribution, offering transparent decision support for healthcare practitioners. Experimentally, the framework achieved 97.56% validation accuracy, 0.98 macro precision, 0.97 macro recall and 0.98 macro F1-score, outperforming a baseline CNN (85.00% accuracy) and a GAN-augmented CNN (95.00% accuracy). Minority class recognition, such as for Moderate Demented improved from near-zero representation to 99% precision and recall due to balanced augmentation. This approach offers a scalable, interpretable, and high-accuracy diagnostic solution with the potential to transform automated neurodegenerative disease detection in clinical and telemedicine settings. 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-7785803\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":534334485,\"identity\":\"938c2c2a-3c2b-4a4b-93aa-f18c4339cb46\",\"order_by\":0,\"name\":\"Talha Shahan 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