Enhancing Alzheimer’s disease diagnosis through multimodal integration of structural MRI and APOE genotyping using deep learning

Abstract Alzheimer’s Disease (AD) is a neurodegenerative disorder that poses a serious threat to the quality of life of the elderly population, and its early diagnosis is crucial for slowing disease progression. This study proposes a novel multimodal classification framework that integrates structural magnetic resonance imaging (sMRI) and APOE genotype data with deep learning techniques to improve AD classification performance. Specifically, three core modules are designed: 1) the Spatial Enhancement Weighting Module (SEWM), which enhances local anatomical feature extraction from sMRI data using channel and spatial attention mechanisms; 2) the Parallel Multi-Scale Feature Fusion Module (PMSFM), which fuses multi-scale features through pixel-level attention mechanisms to capture subtle pathological changes; and 3) the Abstract Patch (AP) module, which compresses high-dimensional data using depthwise separable convolutions and multi-head self-attention mechanisms to reduce computational complexity. Experiments were conducted using data from the ADNI database, with ADNI1, ADNI GO, and ADNI2 as the training set and ADNI3 as the independent validation set. Results show that the proposed method achieves an accuracy of 86.2% in the three-class classification task (CN versus MCI versus AD) and up to 96.2% in the binary classification task (CN versus AD), surpassing existing multimodal models. Further analysis demonstrates that the synergistic effects among the modules effectively enhance the model’s ability to distinguish MCI heterogeneity and AD pathological features. This study provides an efficient technical approach for the early diagnosis of AD. Future work will focus on model lightweighting and cross-center data validation.