| 基于SVM的尿液粒子识别算法研究 |
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| 引用本文： | 傅聪,夏顺仁,张赞超.基于SVM的尿液粒子识别算法研究[J].中国医疗器械杂志,2008,32(6). |
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| 作者姓名： | 傅聪 夏顺仁 张赞超 |
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| 作者单位： | 浙江大学生物医学工程教育部重点实验室,浙江,杭州,310027 |
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| 摘 要： | 本文将支持向量机的算法引入到尿沉渣有形成分的分类问题上.在提取特征的基础上,采用交叉验证法和精度等高线图进行核函数及参数的选择.根据支持向量机和数据集特点,设计出由两级分类器集成的支持向量机多分类器.得到了相应的混淆矩阵.临床实验数据分类评测以及与神经网络方法比较结果表明,提出的算法具有一定的优势.
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| 关 键 词： | 统计学习理论 支持向量机 尿沉渣图像 交叉验证 混淆矩阵 |
The Study of SVM-Based Recognition of Particles in Urine Sediment |
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| FU Cong,XIA Shun-ren,ZHANG Zan-chao.The Study of SVM-Based Recognition of Particles in Urine Sediment[J].Chinese Journal of Medical Instrumentation,2008,32(6). |
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| Authors: | FU Cong XIA Shun-ren ZHANG Zan-chao |
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| Institution: | FU Cong,XIA Shun-ren,ZHANG Zan-chao Key Lab of Biomedical Engineering of Ministry of Education,Zhejiang University,Hangzhou,310027 |
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| Abstract: | 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 compa... |
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| Keywords: | statistical learning theory support vector machine SVM urine sediment cross-validation classification-matrix |
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