Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease
  Diagnosis using Brain Images
Yanteng Zhang Qizhi Teng Xiaohai He Tong Niu Lipei Zhang Yan Liu Chao Ren
Structural MRI and PET imaging play an important role in the diagnosis of
Alzheimer's disease (AD), showing the morphological changes and glucose
metabolism changes in the brain respectively. The manifestations in the brain
image of some cognitive impairment patients are relatively inconspicuous, for
example, it still has difficulties in achieving accurate diagnosis through sMRI
in clinical practice. With the emergence of deep learning, convolutional neural
network (CNN) has become a valuable method in AD-aided diagnosis, but some CNN
methods cannot effectively learn the features of brain image, making the
diagnosis of AD still presents some challenges. In this work, we propose an
end-to-end 3D CNN framework for AD diagnosis based on ResNet, which integrates
multi-layer features obtained under the effect of the attention mechanism to
better capture subtle differences in brain images. The attention maps showed
our model can focus on key brain regions related to the disease diagnosis. Our
method was verified in ablation experiments with two modality images on 792
subjects from the ADNI database, where AD diagnostic accuracies of 89.71% and
91.18% were achieved based on sMRI and PET respectively, and also outperformed
some state-of-the-art methods.