| 基于支持向量机的乳腺癌辅助诊断 |
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| 引用本文： | 刘兴华,蔡从中,袁前飞,肖汉光,孔春阳.基于支持向量机的乳腺癌辅助诊断[J].重庆大学学报(自然科学版),2007,30(6):140-144. |
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| 作者姓名： | 刘兴华 蔡从中 袁前飞 肖汉光 孔春阳 |
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| 作者单位： | 重庆大学数理学院,重庆400030;重庆大学数理学院,重庆400030;新加坡国立大学计算科学系及制药系,新加坡117543;重庆师范大学物理学与信息技术学院,重庆400047 |
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| 基金项目： | 重庆市自然科学基金
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重庆大学与新加坡国立大学国际联合科研项目 |
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| 摘 要： | 采用支持向量机、K-近邻法(K-Nearest Neighbor,K-NN)、概率神经网络(Probabilistic Neural Network,PNN),结合乳腺肿瘤的细针穿刺细胞病理学临床数据诊断乳腺癌.结果表明:当使用sigmoid核函数时,SVM通过5次交叉验证的最佳平均分类准确率达到了96.24%,优于K-NN(95.37%),PNN(95.09%)等分类器,表明该方法有望成为一种实用的乳腺癌临床辅助诊断工具.
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| 关 键 词： | 支持向量机 K-近邻法 概率神经网络 乳腺癌 诊断 模式识别 |
| 文章编号： | 1000-582X（2007）06-0140-05 |
| 修稿时间： | 2007-01-31 |
Computer-aided Diagnosis of Breast Cancer Based on Support Vector Machine |
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| LIU Xing-hu,CAI Cong-zhong,YUAN Qian-fei,XIAO Han-guang,KONG Chun-yang.Computer-aided Diagnosis of Breast Cancer Based on Support Vector Machine[J].Journal of Chongqing University(Natural Science Edition),2007,30(6):140-144. |
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| Authors: | LIU Xing-hu CAI Cong-zhong YUAN Qian-fei XIAO Han-guang KONG Chun-yang |
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| Institution: | 1. College of Mathematics and Physics, Chongqing University, Chongqing 400030, China ;2. Department of Computational Science and Department of Pharmacy, National University of Singapore, Singpore 117543, Singapore ; 3. College of Physics and Information Technology, Chongqing Normal University, Chongqing 400047, China |
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| Abstract: | Combined with the breast fine needle aspiration cytology,the SVM,K-Nearest Neighbor(K-NN) and Probabilistic Neural Network(PNN) are used to diagnose the breast cancer.The best overall accuracy reaches 96.24% via SVM with Sigmoid kernel by using 5-fold cross validation,and is superior to those of other classifiers including K-NN(95.37%) and PNN(95.09%).Support vector machine is capable of being used as a potential application tool for SVM-aided clinical breast cancer diagnosis. |
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| Keywords: | support vector machine K-nearest neighbor probabilistic neural network breast cancer diagnosis pattern recognition |
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