FPRes-Net: Feature Pyramid-Based Residual Network for Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is a neurodegenerative disorder that progresses in a slow and irreversible manner. Although many computer-aided methods have been used to diagnose AD, the issue of underutilization of detailed information and features persists. In this study, we propose a new AD diagnostic network (FPRes-Net) that can fully learn the rich information of 3D MRI slices by extracting multi-scale features and feature fusion. Firstly, in order to fully extract multi-scale information, a network structure combining ResNet-50 with feature pyramids was designed. Next, a feature fusion method was designed to reduce noise and increase the importance of important features. Finally, a visually interpretable method called Gradient-weighted Class Activation Mapping (Grad-CAM) was introduced to visualize important feature regions in AD diagnosis. Experimental analysis was conducted on the publicly accessible ADNI-1 dataset, and our proposed FPRes-Net model performed better than other advanced research methods, with an accuracy rate of 99.5%. Our proposed model can be effectively used for clinical diagnosis of AD.