According to the AD classification prediction method and
system based on the multi-mode multi-layer graph neural
network model, the AD classification performance is improved by combining imaging and non-image information, and the AD classification prediction method and
system have wide application potential in the field of cranial
nerve diseases. According to the method, a multilayer graph neural network of a double-layer GNN architecture based on GAT and GCN is constructed based on the relationship between brain ROIs and the relationship between subjects, hierarchical training is performed on the brain ROI relationship and the subject relationship, and the performance of the model in AD classification is improved. According to the method, the
Gaussian kernel function is adopted to construct the
brain network between the brain ROIs as the ROI features, the process of extracting the
brain region features is simplified, and the training requirement of the GNN is better met. According to the method, the multi-source non-image information matrix is constructed, the graph construction and training process of the second layer GNN is optimized, and the influence of the non-image information on the edge weight is adjusted through back propagation, so that the relationship among subjects is considered more comprehensively.