Alzheimer's disease diagnosis based on the visual attention model and equal‐distance ring shape context features

Alzheimer's disease (AD) is an irreversible neurodegenerative disease caused by rapid degeneration of brain cells. More and more researchers focus on effective and accurate methods for the diagnosis of AD. In this paper, a method to identify AD by extracting equal‐distant ring shape context features from saliency map of structural magnetic resonance imaging (sMRI) is proposed. The experimental results on the thin‐layer MR images of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset showed that our method helped improve the performance of identifying brain diseases. Specifically, the classification accuracy of 94.83% for AD versus CN, 98.31% for AD versus MCI and 85.77% for MCI versus CN, respectively. At the same time, experiments on Open Access Series of Imaging Studies dataset and clinically collected thick‐layer MR images verify the classification performance of the method. The results show that this method may have higher application value in clinical application, with classification accuracies of 96.56% and 98.18% for AD versus CN, respectively. Compared with the methods based on gray matter (GM) density, cortical thickness and hippocampal volume, our method achieved higher accuracy of AD (or MCI) and CN classification.