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.