## 1 Introduction
found that using the patient's height to adjust the renal parenchymal thickness and kidney length, the correlation coefficients against eGFR could be improved to 0.537 and 0.510, respectively. The product of adjusted renal parenchymal thickness and length showed the highest correlation of 0.560 with the eGFR [16]. These sonographic parameters are highly correlated with eGFR, and ultrasonography can be applied as an investigation method to diagnose CKD and its stage progression. [PERSON] et al. developed a CKD scoring system that integrated three ultrasound parameters, kidney length, parenchymal thickness, and echo intensity, and a correlation coefficient of 0.587 was obtained [17]. [PERSON] et al. developed a CKD scoring system that applied machine learning technologies to extract features, including 11 parameters such as visceral length, parenchymal thickness, and echogenicity, and the accuracy of CKD stage predictions reached 70.05% [7]. [PERSON] et al. developed a CNN-based deep learning CKD scoring system based on a model architecture that included 33 residual blocks and three fully connected layers for continuous eGFR prediction. The model achieved a correlation of 0.741 and a mean absolute error (MAE) of 17.605 on the testing dataset. The gradient-boosting tree model for classifying eGFR with a threshold of 60 mL/min/1.73 m2 could achieve an accuracy of 85.6% and an area under the curve (AUC) of 0.904 [18]. At present, most accurate image classification systems usually apply CNN deep learning algorithms, which surpass human experts in a variety of image classification applications. The data for these successful studies were balanced and sufficient. Unfortunately, it is comparatively difficult to obtain sufficient and balanced datasets in real medical image classification applications. Researchers usually face the problem of insufficient and imbalanced datasets for medical image classification. At present, medical imaging-related research must first obtain the IRB agreement and informed consent from patients in ad vance to perform the research, and the cost of acquiring and storing medical images is not low, which is the main reason why the number of medical images is insufficient. Therefore, this research aims to solve the data imbalance problem and to generate sufficient kidney ultrasound images through the generative adversarial network (GAN) [19] and use the generated additional images to solve the problem of data imbalance. GAN captures features from the original dataset and generates new samples that share the same distribution as the original samples. Since the first publication of GAN, various derivative GAN models have been proposed and widely used in many fields. Related studies were introduced in the next section. In this study, we proposed an auxiliary diagnosis system based on deep learning for renal ultrasound images for CKD stage classification. The training data used a collection of 798 kidney ultrasound images from 205 patients with CKD. The deep learning model applied ACWGAN-GP [20,21] and transfer learning [22] to assist doctors in categorizing CKD subjects at different stages based on the obtained ultrasound images. As long as the GAN model learns the potential probability distribution of the collected samples from the training dataset, it can provide a large amount of newly generated training data to improve the prediction accuracy of the classifier. This system provides an effective, objective, and consistent method for CKD stage classification and provides medical staff with a reference basis for practical clinical diagnosis.