Hybrid Attention and Multiscale Module for Alzheimer's Disease Classification

Alzheimer's disease is the most common neurodegenerative disorder among dementia, characterized by slow disease progression and complex imaging features. Traditional image-based diagnostic processes are time-consuming and vary in accuracy. To address these challenges, this study proposes a novel classification method based on hybrid attention and multi-scale information fusion (3D HAMSNet). The method leverages image data and a convolutional neural network to enhance the model's attention to the hippocampus, amygdala, and temporal lobe through the introduction of a hybrid attention mechanism. Additionally, it integrates multiscale spatial scale features of Alzheimer's disease by using a multiscale information fusion module based on dilated convolution and soft attention, enhancing early diagnosis and prediction. Finally, tested on 198 Alzheimer's patients, 200 individuals with mild cognitive impairment, and 139 healthy controls, it achieved 94.14% accuracy, 97.07% specificity, and 94.17% F1 score—represented improvements of 9.88%, 4.94%, and 10.17% over the baseline. The method outperforms existing classification methods and provides a new approach for early Alzheimer's diagnosis.