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

## TL;DR

This paper introduces a deep learning framework called ADMV-Net that improves Alzheimer's disease diagnosis by combining multiple types of brain imaging data.

## Contribution

ADMV-Net is a novel multimodal diagnostic framework that integrates global, local, and regional features for improved Alzheimer's disease classification.

## Key findings

- ADMV-Net achieved 94.83% accuracy and 95.97% AUC in distinguishing Alzheimer's disease from cognitively normal individuals.
- The framework shows strong performance in multi-class classification tasks and outperforms existing methods.
- The proposed framework effectively captures and integrates multimodal features from multiple perspectives.

## Abstract

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.

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.

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.

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.