Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.

With the rapid development of artificial intelligence and medical image analysis, MRI-based automated diagnosis has provided an effective approach for Alzheimer's disease (AD) assessment. To improve the performance of MRI-based AD classification, this study proposes an AD diagnosis model termed High-level CBAM-ResNet34. First, T1-weighted structural MRI data are preprocessed using a unified pipeline and further converted into two-dimensional slices for model training. Then, a ResNet34-based classification framework is constructed, in which the convolutional block attention module (CBAM) is introduced into the high-level feature stage to enhance discriminative feature representation. In addition, to better adapt to inter-dataset differences, the negative-class weight parameter is selected according to the empirical performance on each dataset during training. Experimental results on the ADNI dataset show that the proposed model achieved an AUC of 0.8757 and an accuracy of 0.8160, outperforming several representative comparison models in overall performance. Ablation and comparison experiments further verified the effectiveness of the proposed design, and external validation on the open access series of imaging studies 1 (OASIS 1) dataset demonstrated its generalization ability. These results indicate that the proposed model is effective for MRI-based AD diagnosis and provides a useful reference for computer-aided neuroimaging analysis.