## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Actual\_Predict** & **Stage 1** & **Stage 2** & **Stage 3** & **Stage 4** & **Sensitivity** \\ \hline Stage 1 & 21 & 1 & 0 & 2 & 87.5\% \\ Stage 2 & 3 & 9 & 1 & 0 & 69.2\% \\ Stage 3 & 2 & 0 & 7 & 0 & 77.8\% \\ Stage 4 & 0 & 1 & 1 & 13 & 86.7\% \\ Specificity & 86.5\% & 95.8\% & 96.2\% & 95.7\% & \\ \hline \end{tabular}
\end{table} 
Table 2: Performance of prediction results by using fake images generated by ACWGAN-GP. Figure 4: Fake images generated by ACWGAN-GP. Kidney ultrasound imaging has always been a convenient and fast diagnostic tool in nephrology. At present, the clinical diagnosis of CKD stages highly depends on the experience of nephrologists. Most nephrologists evaluate the renal function and CKD stages based on the thickness of the kidney parenchyma and the intensity of renal echo. However, different nephrologists might provide different diagnoses of CKD stages from an identical ultrasound image, and even the same nephrologist might give inconsistent diagnoses of CKD stages at different times. To provide a consistent assessment of CKD stages, this study proposed a prediction model based on a deep learning algorithm to predict a patient's CKD stage. The proposed method is an objective, rapid, and non-invasive CKD staging assessment. To improve the prediction accuracy of neural network classification, this study established an effective method to classify CKD patients in different stages based on kidney ultrasound images. The proposed method not only applies the CNN network but also provides data augmentation, generative adversarial networks, transfer learning, and other technologies to obtain superior prediction performance. Data augmentation and GAN technologies generated more kidney ultrasound images to increase the number of training samples. The transfer learning technology retained the low-level features learned from the ImageNet dataset. The MobileNet learned from the original training samples and the generated training samples through fine-tuning and constructed a highly accurate CKD ultrasound image prediction model. The non-invasive kidney ultrasound examination can further provide accurate and stable predictions for CKD stages, assisting doctors to quickly diagnose and activate the prognostic treatment or an early warning mechanism for CKD patients. Although the CKD stage prediction by the proposed model cannot be used as a clinical diagnosis alone, this model can provide decision-making support for facilitating doctors' diagnosis or precise treatment for patients at various stages of CKD. It can even discover whether a subject is a potential CKD patient at an early stage so that nephrologists can quickly provide appropriate personal treatment and precision healthcare for patients. In this study, the ROIs of all kidney ultrasound images for training were cropped. Therefore, kidney segmentation from an ultrasound image must be manually cropped before entering it into the training system. Although nephrologists are supposed to mark down kidney locations from severely degraded ultrasound images, this still requires an extra step of manual segmentation for the prediction system. We have demonstrated that kidney ultrasound images could provide a high degree of accuracy for CKD stage classification, and we hope that the precise location of the kidneys within an ultrasound image could be automatically allocated through R-CNN, SSD, YOLO, and other object detection methods. Automatic kidney object recognition can provide and facilitate nephrologists with a simplified approach for consistent and accurate CKD stage classification.