ADLi-Net: A Learnable Dilation Deep Learning Framework for Accurate Alzheimer’s Disease Stage Classification from Brain Magnetic Resonance Images

ADLi-Net: A Learnable Dilation Deep Learning Framework for Accurate Alzheimer’s Disease Stage Classification from Brain Magnetic Resonance 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 ADLi-Net: A Learnable Dilation Deep Learning Framework for Accurate Alzheimer’s Disease Stage Classification from Brain Magnetic Resonance Images Neetha Papanna Umalakshmi, Irshad Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8580717/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Alzheimer’s Disease (AD) is a leading cause of dementia because of its advanced neurodegenerative nature. However, conventional Deep Learning (DL) models that use fixed dilation are struggle to capture both fine-grained and global structural changes, thereby leading to suboptimal feature representations. In this research, a novel framework named Alzheimer’s Disease Learnable Dilation Network (ADLi-Net) is proposed, which integrates DenseNet-201 with a Learnable Atrous Spatial Pyramid Pooling (ASPP) module for multi-class AD classification using Magnetic Resonance Imaging (MRI). The learnable ASPP dynamically learns through backpropagation during training, thereby providing a model for extracting context-aware features from complex brain structures. Preprocessing techniques, such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and min-max normalization are utilized to enhance image contrast and standardize pixel intensity values thereby ensuring consistent input for further process. The proposed ADLi-Net is estimated on the ADNI dataset containing over 23,000 T2-weighted MRI scans categorized into six cognitive states. The experimental results show that ADLi-Net achieves better performance, with an overall accuracy of 98.19% on ADNI dataset. The comparative analysis and ablation study ensured the efficiency of learnable dilation rates, weighted loss functions, and optimal validation strategies. Therefore, this research demonstrates that dynamically tuned multi-scale spatial filtering enhances AD classification. Alzheimer Disease Backpropagation Contrast Limited Adaptive Histogram Equalization Densenet-201 Learnable Atrous Spatial Pyramid Pooling Magnetic Resonance Imaging Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 15 Apr, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers invited by journal 19 Jan, 2026 Editor invited by journal 18 Jan, 2026 First submitted to journal 12 Jan, 2026 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-8580717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576751849,"identity":"e9612a1d-e9da-490a-9d49-65c1d933cd84","order_by":0,"name":"Neetha Papanna Umalakshmi","email":"","orcid":"","institution":"BMS Institute of Technology and Management","correspondingAuthor":false,"prefix":"","firstName":"Neetha","middleName":"Papanna","lastName":"Umalakshmi","suffix":""},{"id":576751850,"identity":"d9b8c026-5d2e-4baa-8277-c1db8596dd88","order_by":1,"name":"Irshad 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