The invention discloses an Alzheimer's disease classification method based on an improved 3D CNN network, and the method carries out the training based on 3D nuclear magnetic resonance data, and achieves the precise brain disease classification. The method mainly comprises the following steps: 1) modeling by using a 3D CNN: replacing a 2D CNN part in a VGG network by using the 3D CNN; 2) optimizing a model structure: firstly adding a batch normalization layer in the model, and then introducing jump connection; 3) experimental data processing: preprocessing experimental data, and dividing a training set and a verification set; 4) model training: inputting the data into a model, training network parameters, and reserving an optimal model. According to the method, multiple jump connections are introduced into the 3DVGG model for Alzheimer's disease diagnosis for the first time, the performance of the method is superior to that of an existing method, and the method has the advantages of being high in universality, high in robustness and the like.
Alzheimer's disease classification method based on improved 3D CNN network