本文围绕基于深度学习的肾结石检测分类和评估问题展开研究，提出了适用于肾

本文围绕基于深度学习的肾结石检测分类和评估问题展开研究，提出了适用于肾脏超声图像分类算法和检测模型，论文选题合理，具有较强的实际应用价值。为了提升肾脏超声图像中肾结石检测准确度，提出了基于深度学习的肾结石检测分类算法，并与EANet 、InceptionV3 和 SqueezeNet 模型进行实验比对分析，实验结果表明，提出的新算法一定程度上提高了检测准确度，为医疗应用领域的辅助诊断系统提供了技术参考。论文结构和实验设计基本合理，阐述基本清晰，工作量基本满足，图表基本规范，认真修改不足之后，基本符合硕士学位论文要求。

Results (English) 1:

This paper focuses on the research on the classification and evaluation of kidney stone detection based on deep learning, and proposes a classification algorithm and detection model suitable for renal ultrasound images. The topic of the paper is reasonable and has strong practical application value. In order to improve the accuracy of kidney stone detection in renal ultrasound images, a kidney stone detection and classification algorithm based on deep learning was proposed, and experimental comparison analysis was conducted with EANet, InceptionV3 and SqueezeNet models. The experimental results show that the proposed new algorithm improves the accuracy to a certain extent. It improves detection accuracy and provides technical reference for auxiliary diagnosis systems in medical applications. The structure and experimental design of the thesis are basically reasonable, the explanation is basically clear, the workload is basically satisfied, the charts are basically standardized, and after careful revision, it basically meets the requirements for a master's degree thesis.

Results (English) 2:

This article focuses on the classification and evaluation of kidney stone detection based on deep learning, and proposes classification algorithms and detection models suitable for kidney ultrasound images. The topic of the paper is reasonable and has strong practical application value. In order to improve the accuracy of kidney stone detection in renal ultrasound images, a deep learning based classification algorithm for kidney stone detection was proposed, and experimental comparisons were conducted with EANet, InceptionV3, and SqueezeNet models. The experimental results showed that the proposed new algorithm improved detection accuracy to a certain extent, providing technical reference for auxiliary diagnostic systems in the medical application field. The structure and experimental design of the paper are basically reasonable, the explanations are basically clear, the workload is basically met, the charts are basically standardized, and after careful modification, the requirements for the master's degree thesis are basically met.

Results (English) 3:

This paper focuses on the classification and evaluation of kidney calculi detection based on deep learning, and puts forward a classification algorithm and detection model suitable for renal ultrasound images. The topic of this paper is reasonable and has strong practical application value. In order to improve the accuracy of kidney calculi detection in renal ultrasound images, a kidney calculi detection classification algorithm based on deep learning is proposed, and compared with EANet, InceptionV3 and SqueezeNet models. The experimental results show that the proposed new algorithm improves the detection accuracy to some extent, which provides technical reference for the auxiliary diagnosis system in medical applications. The structure and experimental design of the paper are basically reasonable, the exposition is basically clear, the workload is basically satisfied, the chart is basically standardized, and after careful revision, it basically meets the requirements of the master's degree thesis.