Browsing by Author "Manal, Zafeera"

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    Classification of Alzheimer’s and Dementia subtypes using R-STDP driven spiking neural networks
    (BRAC University, 2025-10) Fariha, Anika; Tasnim, Noshin Fouzia; Manal, Zafeera; Ira, Rayatun Tehrin; Tanzeem, Eshat; Alam, Md. Golam Rabiul; Humayun, Zayed
    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.