Alam, Md. Golam RabiulHumayun, ZayedFariha, AnikaTasnim, Noshin FouziaManal, ZafeeraIra, Rayatun TehrinTanzeem, Eshat2025-12-292025-12-2920252025-10ID 22101305ID 21201236ID 22141019ID 22101433ID 22101328http://hdl.handle.net/10361/27383Cataloged from PDF version of thesis.Includes bibliographical references (pages 48-51).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.Early classification of Dementia which can further lead to Alzheimer’s disease remains challenging due to subtle brain structural changes in MRI scans. This paper presents a novel neuromorphic feature extraction approach combining biologically inspired temporal encoding with Forward-Forward learning for the classification of Alzheimer’s and dementia subtypes. Our methodology employs a three-stage pipeline: neuromorphic preprocessing converts 32×32 brain MRI data into temporal spike patterns across 20 time steps, incorporating skull stripping and CLAHE enhancement; Forward-Forward learning with Reward-based Spike-Timing-Dependent Plasticity (R-STDP) autoencoder extracts latent features without traditional backpropagation; ensemble classification using Random Forest and Gradient Boosting provides final predictions. The neuromorphic preprocessor generates 8,192- dimensional feature vectors capturing temporal dynamics including first spike timing, burst detection, temporal phases, and activity statistics. The Forward-Forward R-STDP autoencoder learns biologically-plausible representations through positivenegative sample discrimination with a 256-dimensional latent bottleneck. Advanced feature selection reduces combined features to around 3000 optimal dimensions through variance filtering, statistical selection, and recursive feature elimination. Our system achieves 77.92% ensemble accuracy on Demented vs. NonDemented classification, with 79.47% weighted precision and 77.69% out-of-bag score. Random Forest achieves 78.77% accuracy while Gradient Boosting reaches 76.93%. Our neuromorphic approach allows parallel processing, reducing computational overhead compared to conventional deep learning and using biologically-inspired representations to capture temporal patterns in brain imaging data. This framework aims to provide an energy-efficient alternative to traditional deep learning while ensuring that a robust classification performance is maintained for neurodegenerative disease detection.62 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Neurodegenerative diseasesDisease detectionAlzheimer’s diseaseDementiaSNNSpiking neural networksForward-forward learningMRIbased DementiaR-STDPAlzheimer's disease--Classification--Data processing.Dementia--Diagnosis, Differential.Neural networks (Computer science).Computational neuroscience.Alzheimer's disease--Early detection.Thesis