## 3 Experiments
### 3D-CNN Learning Process Visualization  
There have been many previous studies [27; 28] on deep learning model explanation and on deep learning visualization, among which the most well known is CAM. CAM shows the basis of its decision in the form of a heat map when a model is needed to explain the reason for its classification, such as when informing where there are focal points in the map. For a deep CNN, after multiple convolutions and pooling, the last convolutional layer contains the richest spatial and semantic information, and the next layers are the fully connected layer and softmax layer, which contain information that is difficult for humans to understand and display in a visual way. Therefore, in order to provide a reasonable explanation of the classification results of the convolutional neural network, it is necessary to make full use of the last convolutional layer, and CAM draws on the idea of the well-known paper on Network in Network [27], which uses GAP (global average pooling) to replace the fully connected layer. GAP can be considered as a special average pooling layer, except that its pool size is as large as the whole feature map, which is actually the average value of all pixels in each feature map. This greatly limits its use. If the model is already online or the training cost is very high, it is almost impossible to retrain it. The basic idea of Grad-cam is the same as that of CAM, which is to obtain the weights of each pair of feature maps and then find a weighted sum. CAM replaces the fully connected layers with GAP layers and retrains the weights, whereas Grad-cam takes a different approach and uses the global average of gradients to calculate the weights. Although Grad-cam and other similar methods are effective, they have limitations, such as the localization of multiple similar targets at the same time; even for a single object, Grad-cam cannot localize it completely. Based on Grad-cam, the authors of [28] proposed Grad-cam++, which improved the previous method, with the main contribution that ++ introduced a pixel-level weighting of the output gradient for a specific location. This method provides a measure of the importance of each pixel in the feature map, and more importantly, they derived closed-form solutions while obtaining exact higher-order representations, including softmax and exponential activation outputs. Our method requires one back-propagation, so the computational effort is consistent with the previous gradient-based method, but the results are more effective. It can be extended in the field of 3D deep learning visualization. In this paper, we used Grad-cam++ in a 3D image sequence, as shown in Figure 4; we found that the model accurately focused on the cancerous tissue by using the focus map and heat map, and it learned the feature details of the cancerous tissue.  
Figure 4 shows the heat map obtained through 3D visualization, where the red area represents the area where the network model focused and learned features, and the area closest to the red indicates the area where the convolutional network focused more of its attention. From the figure, we can see that the areas on which the network model focused were the lesion areas.