Abstract: The auxiliary diagnosis of Alzheimer’ s disease (AD) has long been an important research topic in brain science. This paper proposed a novel deep learning model termed AttentionConvFusion (ACF). By introducing a self-attention mechanism that dynamically optimizes convolutional kernel weights, the model can more effectively capture critical features in neuroimaging data. Experimental results show that the proposed ACF model achieves satisfactory performance across different AD classification tasks. Specifically, in the classification of AD versus normal cognition, the ACF model achieves an accuracy of 81.5%. For AD versus mild cognitive impairment, it achieves an accuracy of 80.1%. In the multi-class classification task, it achieves an accuracy of 77.8%. These results indicate that the proposed ACF model has significant advantages in the diagnosis and classification of AD.

Key words: deep learning, Alzheimer’s disease, convolutional neural network, Transformer, image classification, computer-aided diagnosis