CLASSIFICATION OF ALZHEIMER’S DISEASE BASED ON LIGHTWEIGHT MODEL

Aimed at the problem that the existing Alzheimer’s disease image classification model is too large to be deployed to the mobile terminal, a lightweight model is proposed. Based on the ResNet18 model, the following improvements were made: the model structure was adjusted and the depth separable convolution was used to replace part of the common convolution to reduce the number of parameters in the model; the void space pyramid (ASPP) module was improved to enhance the ability of the model to capture global context information. The embedding of attention module CBAM improved the efficiency of feature extraction. The experimental results show that compared with the model ResNet18, the number of parameters of the improved model is reduced by 95.51%, and the classification accuracy and F1 score are increased by 1.67 and 2.20 percentage points, respectively, which has higher application value.