A multi-view multimodal deep learning framework for Alzheimer's disease diagnosis

Introduction Early diagnosis of Alzheimer's disease (AD) remains challenging due to the high similarity among AD, mild cognitive impairment (MCI), and cognitively normal (CN) individuals, as well as confounding factors such as population heterogeneity, label noise, and variations in imaging acquisition. Although multimodal neuroimaging techniques like MRI and PET can provide complementary information, current approaches are limited in multimodal fusion and multi-scale feature aggregation. Methods We propose a novel multimodal diagnostic framework, Alzheimer's Disease Multi-View Multimodal Diagnostic Network (ADMV-Net), to enhance recognition accuracy across all AD stages. Specifically, a dual-pathway Hybrid Convolution ResNet module is designed to fuse global semantic and local boundary information, enabling robust three-dimensional medical image feature extraction. Furthermore, a Multi-view Fusion Learning mechanism, which comprises a Global Perception Module, a Multi-level Local Cross-modal Aggregation Network, and a Bidirectional Cross-Attention Module, is introduced to efficiently capture and integrate multimodal features from multiple perspectives. Additionally, a Regional Interest Perception Module is incorporated to highlight brain regions strongly associated with AD pathology. Results Extensive experiments on public datasets demonstrate that ADMV-Net achieves 94.83% accuracy and 95.97% AUC in AD versus CN classification, significantly outperforming mainstream methods. The framework also shows strong discriminative capability and excellent generalization performance in multi-class classification tasks. Discussion These findings suggest that ADMV-Net effectively leverages multimodal and multi-view information to improve the diagnostic accuracy of AD. By integrating global, local, and regional features, the framework provides a promising tool for assisting early diagnosis and clinical decision-making in Alzheimer's disease. The implementation code is publicly available at https://github.com/zhaoxinyu-1/ADMV-Net .