Martinez Adriana, Trinh Dinh-Hoan, El Beze Jonathan, Hubert Jacques, Eschwege Pascal, Estrade Vincent, Aguilar Lina, Daul Christian, Ochoa Gilberto
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1936-1939. doi: 10.1109/EMBC44109.2020.9176121.
Urolithiasis is a common disease around the world and its incidence has been growing every year. There are various diagnosis techniques based on kidney stone identification aiming to find the formation cause. However, most of them are time consuming, tedious and expensive. The accuracy of the diagnosis is crucial for the prescription of an appropriate treatment that can eliminate the stones and diminish future relapses. This paper presents two effective supervised learning methods to automate and improve the accuracy of the classification of kidney stones; as well as a dataset consisting of kidney stone images captured with ureteroscopes. In the proposed methods, the image features that are visually exploited by urologists to distinguish the type of kidney stones are analyzed and encoded as vectors. Then, the classification is performed on these feature vectors through Random Forest and ensemble K Nearest Neighbor classifiers. The overall classification accuracy obtained was 89%, outperforming previous methods by more than 10%. The details of the classifier implementation, as well as their performance and accuracy, are presented and discussed. Finally, future work and improvements are proposed.
尿石症是一种全球常见疾病，其发病率逐年上升。有多种基于肾结石识别的诊断技术，旨在找出结石形成的原因。然而，其中大多数技术耗时、繁琐且成本高昂。诊断的准确性对于开具能够消除结石并减少未来复发的适当治疗方案至关重要。本文提出了两种有效的监督学习方法，以实现肾结石分类的自动化并提高其准确性；还介绍了一个由输尿管镜拍摄的肾结石图像组成的数据集。在所提出的方法中，分析了泌尿科医生在视觉上用于区分肾结石类型的图像特征，并将其编码为向量。然后，通过随机森林和集成K近邻分类器对这些特征向量进行分类。获得的总体分类准确率为89%，比之前的方法高出10%以上。文中展示并讨论了分类器实现的细节及其性能和准确性。最后，提出了未来的工作和改进方向。
