Patch-Based Siamese 3D Convolutional Neural Network for Early Alzheimer's Disease Using Multi-Modal Approach

Abstract

Alzheimer's Disease (AD), the most common type of dementia, is characterized by memory issues that worsen with time. When evaluating single modalities, there are specific efficient learning techniques that produce a poor identification rate. The clinical significance of Mild Cognitive Impairment (MCI) has clinical importance, which is crucial for identifying and categorizing AD patients. This study includes the utilization of 3D T1 weighted /Magnetic Resonance Imaging (3D T1WI MRI) images where the structural changes in the brain are identified for predicting early AD. The classification accuracy for AD, MCI, and Normal Control (NC) has also increased due to cognitive evaluations when comparing three one vs one classifications, i.e. AD vs. NC, MCI vs. NC, AD vs. MCI, and multi-class classification. The proposed approach’s novelty is a binary and multi-class classification of structural MRI images using a Siamese 3D Convolutional Neural Network (Siamese 3D-CNN). The Stochastic Gradient Descent (SGD) has been employed to modify the proposed network's weights, and 5-Fold Cross-Validation (CV) has been used for each binary classification task. Our study used the neuropsychological data of 417 people from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the suggested methodology. Experimental results demonstrate that accuracy, sensitivity, and specificity for three binary classifications produce the maximum percentage, in contrast to other relevant approaches found in the literature. In addition, the multi-class classification accuracy of 99.8% was attained.