Multi-View Separable Residual convolution neural Network for detecting Alzheimer's disease progression

Alzheimer's Disease (AD) is a neurodegenerative disorder, the most common form of dementia, characterized by memory loss and cognitive impairments that disrupt daily life. Early detection is crucial for effective treatment. 18F-FDG-PET is the most accurate diagnostic tool, but existing methods often rely on handcrafted or machine learning, risking information loss due to preprocessing. To overcome these limitations, we propose a deep learning approach, leveraging Convolutional Neural Networks (CNNs). We introduce a novel Multi-View Separable Residual CNN (MV-SR-CNN) architecture, capable of processing entire volumes while maintaining spatial complexity similar to 2D CNNs. MV-SR-CNN considers voxel spatial relationships and achieves up to 50 % memory reduction compared to 3D CNNs. We evaluated MV-SR-CNN on a dataset of 540 patients : 191 Control Normal (CN), 145 Early Mild Cognitive Impairment (EMCI), 122 Late Mild Cognitive Impairment (LMCI), and 82 with AD. Additionally, 397 Stable MCI (SMCI) and 61 Progressive MCI (PMCI) cases were included. MV-SR-CNN achieved impressive accuracies of 86.97 % for CN vs. EMCI vs. LMCI vs. AD and 95.73 % for SMCI vs. PMCI classification tasks.