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Structural MRI and PET imaging play an important role in the diagnosis ofAlzheimer's disease (AD), showing the morphological changes and glucosemetabolism changes in the brain respectively. The manifestations in the brainimage of some cognitive impairment patients are relatively inconspicuous, forexample, it still has difficulties in achieving accurate diagnosis through sMRIin clinical practice. With the emergence of deep learning, convolutional neuralnetwork (CNN) has become a valuable method in AD-aided diagnosis, but some CNNmethods cannot effectively learn the features of brain image, making thediagnosis of AD still presents some challenges. In this work, we propose anend-to-end 3D CNN framework for AD diagnosis based on ResNet, which integratesmulti-layer features obtained under the effect of the attention mechanism tobetter capture subtle differences in brain images. The attention maps showedour model can focus on key brain regions related to the disease diagnosis. Ourmethod was verified in ablation experiments with two modality images on 792subjects from the ADNI database, where AD diagnostic accuracies of 89.71% and91.18% were achieved based on sMRI and PET respectively, and also outperformedsome state-of-the-art methods.

Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images