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

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.