## 5 Results and Discussion  
To verify the transfer learning with the ACWGAN-GP method described in Section 4, we applied several different classification methods to automatically classify the four-stage CKD ultrasound images to obtain the prediction accuracy of each method. The eGFR threshold settings for the four CKD stages were defined as 15, 30, 60 mL/min/1.73 m\({}^{2}\). The classification was evaluated in two ways. The first method is based on evaluating whether the predicted stage is the same as the annotated stage by two professional doctors, while the second method refers to a more flexible verification approach in which the predicted CKD stage could be allowed for one higher-level tolerance. For example, when a subject is diagnosed in the second stage, if the prediction system returns either the second or third stage, both predictions would be considered as acceptable predictions. Essentially, it would be helpful for both patients and doctors to carefully evaluate and re-examine the true condition of patients. In other words, it might be beneficial for early processes regarding precision prevention and treatment. TensorFlow was applied to perform training procedures for all the designed models. Six different training modules including 4-layer CNN, ResNet50, MobileNetV2, ACWGAN-GP, ACWGAN-GP + ResNet50, and ACWGAN-GP + MobileNetV2, were performed. The first 4-layer CNN was trained from scratch using the original dataset, ResNet50 and MobileNetV2 pre-trained models fine-tuned with the original dataset; ACWGAN-GP is a generative adversarial network that constructs generators and classifiers simultaneously; ACWGAN-GP + ResNet50, ACWGAN-GP + MobileNetV2 adopted pre-trained models and applied the original images and generated images (Figure 4) for fine-tuning. The prediction results and a comparison of each prediction model are presented in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Accuracy Allows Deviation (\%)** \\ \hline
4-layer CNN & 63.9 & 80.3 \\ ResNet50 & 59.0 & 77.0 \\ MobileNetV2 & 72.1 & 78.6 \\ ACWGAN-GP & 72.1 & 77.0 \\ ACWGAN-GP + ResNet50 & 65.6 & 88.5 \\ ACWGAN-GP + MobileNetV2 & 81.9 & 90.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the prediction accuracy rates of six different prediction models used in the stage classification of chronic kidney disease. It can be noticed that after the ACWGAN-GP training, the generated images and real images were sent to a pre-trained model MobileNetV2. The proposed ACWGAN-GP + MobileNetV2 model reached an average specificity of 93.6%, average sensitivity of 80.3%, average positive predictive value (precision) of 81.8%, average F1-score of 81.0%, and an accuracy of 81.9%. While allowing a higher stage tolerance prediction of CKD stages, the prediction accuracy can be increased to 90.1%. In other words, if a subject was diagnosed in the first stage condition, it was allowed to be predicted as the first or the second stage; a subject annotated as the second stage is allowed to be predicted as the second or the third stage; a subject annotated as the third stage is allowed to be predicted as the third or the fourth stage. All these higher stage tolerances are counted as correct predictions. The main reason for allowing such a higher stage tolerance prediction is that a severe condition prediction can effectively remind patients and doctors that they should notice the warning message as early as possible, and provide early diagnosis and treatment for precise prevention. As shown in the confusion matrix in Table 2, we observed that the MobileNet model showed a relatively good accuracy rate in the first CKD stage, which was mainly due to a greater number of samples in the first stage than in other stages. The results of this classification model showed that more than 50% of the misclassified samples only hold a one-stage difference, which confirms the effectiveness of the kidney ultrasound images generated from the proposed GAN model for CKD stage classification.