Fu Cong, Xia Shun-Ren, Zhang Zan-Chao
Key Lab of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027.
Zhongguo Yi Liao Qi Xie Za Zhi. 2008 Nov;32(6):409-12.
This article used support vector machine (SVM) algorithm to recognize the particles in urine sediment in this paper. After feature extraction, cross-validation method and the contour chart of the accuracy were implemented to select the kernel function and the parameters of SVM, and according to the characteristics of SVM classifier and sample data, Multi-SVMs with two-level-classifier was successfully designed and A classification matrix was eventually obtained. The evaluation by using clinical data and comparative results with the artificial neural network have demonstrated that the proposed algorithm gets better results.
本文采用支持向量机（SVM）算法识别尿沉渣中的颗粒。在特征提取之后，运用交叉验证方法和准确率等高线图来选择SVM的核函数及参数，并根据SVM分类器和样本数据的特点，成功设计了具有两级分类器的多支持向量机，最终得到了一个分类矩阵。利用临床数据进行的评估以及与人工神经网络的对比结果表明，所提出的算法取得了更好的效果。
