Gradient-weighted class activation maps
Grad-CAM59 in the Pytorch framework was used to visualize the salient features that contributed the most to the prediction output of the model. Given a target category, the Grad-CAM performed back-propagation to obtain the final CNN feature maps and the gradient of the feature maps; only pixels with positive contributions to the specified category were retained through the ReLU function. The Grad-CAM method was used for all test data set (X-data and CT-data) in the CNNCF without changing the framework structure to obtain a visual output of the framework’s high discriminatory ability.