The invention discloses an Alzheimer's 
disease classification method and 
system based on an 
anatomical landmark and a residual network, and the method comprises the steps: carrying out the 
tissue segmentation modulation of a training image set, and obtaining a gray matter image; comparing voxels of the Alzheimer's 
disease image and the normal subject image in the training image set to identify ananatomical 
landmark; taking the obtained 
anatomical landmark as a center, and extracting 
grey matter blocks of the gray image; and connecting the 
grey matter blocks, inputting the obtained synthetic blocks into a residual error 
network model for 
feature extraction, taking features extracted by the residual error 
network model as input of a classifier, and classifying Alzheimer's 
disease patients and normal subjects in the test image set. The 
anatomical landmark is taken as the center, feature blocks of gray matters in three tissues of the brain are taken as the input of the residual network, the feature blocks are taken as the feature expression of each MR image, the residual 
network model is adopted to enhance the 
feature learning capability of the network, and the classification accuracyis enhanced.