A deep residual attention-Recurrent model for early and multi-stage Alzheimer's disease detection.

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that requires accurate and early diagnosis. Deep learning methods have shown significant potential for automated MRI-based AD classification. Methods: A hybrid deep learning framework integrating ResNet152V2, Convolutional Block Attention Module (CBAM), and Bidirectional Gated Recurrent Unit (Bi-GRU) was developed for three-class classification of Normal Cognition (NC), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD). The model was trained using 2,100 ADNI subjects and externally validated using 900 OASIS subjects. Results: The proposed framework achieved 94.5% classification accuracy with an AUC of 95.0% on the ADNI dataset and 92.8% accuracy on the OASIS dataset. Comparative analyses demonstrated improvements of 5.2-7.0% over baseline models. Ablation studies confirmed the contribution of CBAM and Bi-GRU to overall performance. Discussion: The integration of deep residual feature extraction, attention-based refinement, and sequential modeling effectively captures disease-related anatomical patterns. The results suggest that the proposed framework may support automated multi-stage Alzheimer's disease classification and provide a foundation for future computer-aided diagnostic systems.