A self-attention mechanism based model is proposed for the prediction of Alzheimer's disease(AD).Magnetic resonance imaging(MRI)images were pre-processed to extract primary features for brain anatomical structures.A self-attention mechanism based feature processing unit(SAFPU)was designed,and by the theory of residual blocks,multiple SAFPUs were stacked to build a reliable network for predicting AD,which could automatically analyze the dependencies of different brain anatomical structures to generate high-level features for MRI images.The empirical results demonstrate the proposed model outperforms existing AD classification methods,which achieves 99.36％(98.90％)of the maximum accuracy for the AD(early stage of AD,i.e.,Mild Cognitive Impairment)classification task.
维普
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