FPGA based Implementation of Neuromorphic Neural Networks for Early Detection of Alzheimer’s Disease

FPGA based Implementation of Neuromorphic Neural Networks for Early Detection of Alzheimer’s Disease | 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 FPGA based Implementation of Neuromorphic Neural Networks for Early Detection of Alzheimer’s Disease B A Sujathakumari, Usha Rani C M, M S Shalini, N S Sneha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7944463/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) is a progressive, neurodegenerative condition causing cognitive decline with memory loss. To improve life quality and allow intervention in time, detection that is early and accurate is key, especially at Mild Cognitive Impairment (MCI). This paper proposes a Brain-Inspired Neuromorphic approach to classifying Alzheimer’s stages using advanced neural network models, that are Convolutional Neural Networks (CNN), Liquid Neural Networks (LNN), Spiking Neural Networks (SNN), and a hybrid CNN + LNN architecture. MRI datasets used within this study are obtained directly from Alzheimer’s Disease Neuroimaging Initiative (ADNI), which provides high-quality structural imaging data. Using the SPM toolbox, the raw DICOM images are converted into NIfTI format and preprocessed to segment and standardize the data. Dual-Tree Complex Wavelet Transform (DTCWT) is applied in feature extraction for preservation of both spatial with frequency information. These features are then used to train the neural network models in order to classify images into three stages. The trained models get translated into the RTL design as simulated using Xilinx AMD Vivado for exploring hardware - software co design deployment. This hardware-optimized Neuromorphic approach aims to provide a low-power, high-speed, and accurate Alzheimer’s detection solution, connecting research with practical, deployable medical tools. Alzheimer's Disease AD Cognitive Normal CN Mild Cognitive Impairment MCI CNN LNN SNN Verilog Xilinx AMD Vivado 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-7944463","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":540095930,"identity":"9cc3c4e9-3b4d-4a90-ad48-28a2277d7d12","order_by":0,"name":"B A 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