Classification of Alzheimer’s Disease Using Deep Convolutional Spiking Neural Network

Diagnosing Alzheimer’s Disease (AD) in older people using magnetic resonance imaging (MRI) is quite hard since it requires the extraction of highly discriminative feature representation from similar brain patterns and pixel intensities. However, deep learning techniques possess the capability of extracting relevant representations from data. In this work, we designed a novel spiking deep convolutional neural network-based pipeline to classify AD using MRI scans. We considered three MRI scan groups (patients with AD dementia, Mild Cognitive Impairment (MCI), and healthy controls (NC)). We developed a three-binary classification task (AD vs. NC, AD vs. MCI, and NC vs. MCI) for the AD classification tasks. Specifically, an unsupervised convolutional Spiking Neural Networks (SNN) is pre-trained on the MRI scans. Finally, a supervised deep Convolution Neural Network (CNN) is trained on the output of the SNN for the classification tasks. Experiments are performed using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and promising results are obtained for the AD classification tasks. We present our proposed model results for both the unsupervised spike pre-training technique and the case where the pre-training technique was not considered, thus serving as a baseline. The accuracy of the proposed model with spike pre-training techniques for the three-binary classification are 90.15%, 87.30%, and 83.90%, respectively, and the accuracy of the model without the spike are 86.90%, 83.25%, and 76.70%, respectively, with a noticeable increase in accuracy and thus, reveals the effectiveness of the proposed method. We also evaluated the robustness of our proposed approach by running experiment on six baseline methods using our preprocessed MRI scans. Our model outperformed almost all the comparable methods due to the robust discriminative capability of the SNN in extracting relevant AD features for the AD classification task.
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