目的:肾衰竭的主要原因是慢性和多囊肾疾病。囊肿,石头,和肿瘤的发展导致慢性肾脏疾病,通常损害肾功能。肾脏疾病是无症状的,并且在其初始阶段没有任何明显的症状。因此,早期诊断肾脏疾病是防止肾功能丧失和肾衰竭的必要条件。
方法:本文提出了一种计算机辅助诊断(CAD)系统,用于从超声图像中检测多类肾脏异常。提出的CAD系统使用预训练的ResNet-101模型来提取特征和支持向量机(SVM)分类器用于分类目的。超声图像通常会受到斑点噪声的影响,斑点噪声会降低CAD系统的图像质量和性能。因此,有必要从超声图像中去除斑点噪声。因此,提出了一种基于CAD的系统,该系统使用深度残差学习网络(RLN)来降低斑点噪声。使用深度RLN对超声图像进行预处理有助于大幅改善CAD系统的分类性能。与现有的最新方法相比,所提出的CAD系统实现了更好的预测结果。
结果:为了验证提出的CAD系统性能,实验已在嘈杂的肾脏超声图像中进行。与现有方法相比,设计的系统框架实现了最大的分类精度。基于与K近邻等各种分类器的性能比较,为CAD系统选择SVM分类器,树,判别式,天真的贝叶斯,和线性。
结论:与现有最先进的方法相比,所提出的CAD系统在对噪声肾超声图像进行精确分类方面优于现有技术。Further,根据选择性和灵敏度评分来评估CAD系统。所提出的具有预处理模块的CAD系统将用作用于从超声图像诊断多类肾脏异常的实时支持工具。
OBJECTIVE: The primary causes of kidney failure are chronic and polycystic kidney diseases. Cyst, stone, and tumor development lead to chronic kidney diseases that commonly impair kidney functions. The kidney diseases are asymptomatic and do not show any significant symptoms at its initial stage. Therefore, diagnosing the kidney diseases at their earlier stage is required to prevent the loss of kidney function and kidney failure.
METHODS: This paper proposes a computer-aided diagnosis (CAD) system for detecting multi-class kidney abnormalities from ultrasound images. The presented CAD system uses a pre-trained ResNet-101 model for extracting the features and support vector machine (SVM) classifier for the classification purpose. Ultrasound images usually gets affected by speckle noise that degrades the image quality and performance of the CAD system. Hence, it is necessary to remove speckle noise from the ultrasound images. Therefore, a CAD based system is proposed with the
despeckling module using a deep residual learning network (RLN) to reduce speckle noise. Pre-processing of ultrasound images using deep RLN helps to drastically improve the classification performance of the CAD system. The proposed CAD system achieved better prediction results when compared to the existing state-of-the-art methods.
RESULTS: To validate the proposed CAD system performance, the experiments have been carried out in the noisy kidney ultrasound images. The designed system framework achieved the maximum classification accuracy when compared to the existing approaches. The SVM classifier is selected for the CAD system based on performance comparison with various classifiers like K-nearest neighbour, tree, discriminant, Naive Bayes, and linear.
CONCLUSIONS: The proposed CAD system outperforms in classifying the noisy kidney ultrasound images precisely as compared to the existing state-of-the-art methods. Further, the CAD system is evaluated in terms of selectivity and sensitivity scores. The presented CAD system with the pre-processing module would serve as a real-time supporting tool for diagnosing multi-class kidney abnormalities from the ultrasound images.