## 3 Results and Discussion  
### Experimental Data  
The neural network selects AlexNet, VGG19, ResNet101, and DenseNet 121 networks to train the model. The data sets were randomly divided into training and test sets in a 70:30 ratio. The training set is used to train and control the model, while the test set is used to test the accuracy of the classification model. The following figure shows the changes in classification accuracy curves of different networks for different data sets. The abscissa epoch value is the training time, and the vertical coordinate accuracy and loss values are the values of the classification accuracy and loss function, respectively. When the accuracy value is higher, the accuracy value is higher, and the loss value is lower; this shows that the classifier works better in the relationship between the input data and output target, which is abbreviated as acc accuracy. CE in the figure represents the cross-entropy-loss function cross-entropy loss, FL is the loss function focal loss introduced in this paper for data imbalance, data represents the classification accuracy of network training using data-augmentation data sets. Figures 5 and 6 show the training situation of the AlexNet network and VGG network. In the training process of this network, selecting a larger learning rate will result in the overfitting of the network and lead to a decrease in the classification accuracy. Therefore, in the experiment, the learning rates of 0.000001 and 0.000003 were selected for the two networks, respectively. These could achieve higher classification progress without the overfitting phenomenon. In addition, it can be observed from the data curve in the figure that the introduction of the focal-loss function could effectively improve the classification accuracy. However, the data set using the data-augmentation method did not have the expected effect but  
Figures 7 and 8 show the line graphs of the ResNet network and DenseNet network training. It can be observed from the data in the figure that the classification accuracy was be improved by using the focal-loss function. Because there were few samples in this experiment, the accuracy of classification was not much improved compared with the cross-entropy-loss function, and regarding the AlexNet network and VGG network, the use of data-augmentation data set training also decreased the classification progress, but its decrease was smaller than the AlexNet network and VGG network. Figure 9 reflects the comparison of the classification accuracy of four networks in three cases, and it can be observed that the DenseNet network could achieve high classification accuracy in three cases. Figure 8: Variation in training classification accuracy curve and loss value diagram of DenseNet network. Figure 7: Curve variation and loss value diagram of ResNet network training classification accuracy. Figure 9: Four types of network classification accuracy training situations; (**a**) original loss function training network; (**b**) oversampled data set training network. Table 3 documents that the four networks tend to level out classification accuracy at loss values. The testing classification accuracy of the four networks was 89.20% when the cross-entropy-loss function was used, and 92.80% when focal loss was used as the loss function, indicating that the use of the focal-loss function could divide the data set sample weight and reduce the classification accuracy decline caused by data imbalance. However, after training the networks with the data-augmentation data set, the testing classification accuracy of all four networks decreased, and the average value decreased to 69.17%. Among them, the AlexNet network and VGG network received the most impact, followed by the ResNet network, and the DenseNet network received the least impact. For the four types of network in this experiment, two methods of data-balance processing could effectively reduce the loss value, and strengthen the learning ability of the network, so that the network could learn more content.