Siamese Capsule Network (SNNCap): Cognitive Analysis for Alzheimer's Disease Classification From MRI Data

Alzheimer's Disease (AD) detection is essential for timely treatment and better patient care. Magnetic Resonance Imaging (MRI) is a technique in which radio waves and magnetic fields are used to capture high-resolution, multi-dimensional representations of brain structures. This high-resolution imaging capability makes MRI a key tool for diagnosing neurological disorders such as Alzheimer's disease. However, the problem is to correctly classify the fresh MRI scans of patients. Researchers have proposed a deep learning-based method for Alzheimer's disease diagnosis using a Siamese Convolutional Neural Network (SCNN) with three ResNet-34 branches trained on structural MRI data. However, this method relies solely on ResNet34 for feature extraction which struggles to preserve spatial relationship due to pooling operations, causing loss of positional information. Other researchers have explored methods like attention mechanisms and 3D convolutional networks to capture spatial dependencies. However, these methods underperform by missing brain complexity or needing high resources without consistent accuracy. In this study, we propose a cognitively inspired approach for classifying MRI images as Non Demented, Very Mild Demented, Mild Demented and Moderate Demented using Siamese Capsule Network (SNNCap). SNNCap uses ResNet-18 for feature extraction and capsule layers to preserve spatial and part-whole relationships in the images. It compares a test image against a few known reference examples per class. This reference-based validation closely mimics cognitive reasoning, improving the system's generalizability. The model achieves strong results on unseen data and demonstrates its effectiveness through classification reports and confusion matrices.