Lin WuMei, Yuan Xin, Yuen Powing, Wei William I, Sham Jonathan, Shi PengCheng, Qu Jianan
Hong Kong University of Science & Technology, Department of Electrical & Electronic Engineering, Clear Water Bay, Kowloon, Hong Kong, China.
J Biomed Opt. 2004 Jan-Feb;9(1):180-6. doi: 10.1117/1.1628244.
An algorithm based on support vector machines (SVM), the most recent advance in pattern recognition, is presented for use in classifying light-induced autofluorescence collected from cancerous and normal tissues. The in vivo autofluorescence spectra used for development and evaluation of SVM diagnostic algorithms were measured from 85 nasopharyngeal carcinoma (NPC) lesions and 131 normal tissue sites from 59 subjects during routine nasal endoscopy. Leave-one-out cross-validation was used to evaluate the performance of the algorithms. An overall diagnostic accuracy of 96%, a sensitivity of 94%, and a specificity of 97% for discriminating nasopharyngeal carcinomas from normal tissues were achieved using a linear SVM algorithm. A diagnostic accuracy of 98%, a sensitivity of 95%, and a specificity of 99% for detecting NPC were achieved with a nonlinear SVM algorithm. In a comparison with previously developed algorithms using the same dataset and the principal component analysis (PCA) technique, the SVM algorithms produced better diagnostic accuracy in all instances. In addition, we investigated a method combining PCA and SVM techniques for reducing the complexity of the SVM algorithms.
提出了一种基于支持向量机（SVM）的算法，这是模式识别领域的最新进展，用于对从癌组织和正常组织采集的光诱导自体荧光进行分类。在常规鼻内镜检查期间，从59名受试者的85个鼻咽癌（NPC）病变和131个正常组织部位测量了用于开发和评估SVM诊断算法的体内自体荧光光谱。采用留一法交叉验证来评估算法的性能。使用线性SVM算法区分鼻咽癌和正常组织的总体诊断准确率为96%，灵敏度为94%，特异性为97%。使用非线性SVM算法检测NPC的诊断准确率为98%，灵敏度为95%，特异性为99%。与先前使用相同数据集和主成分分析（PCA）技术开发的算法相比，SVM算法在所有情况下都产生了更好的诊断准确率。此外，我们研究了一种结合PCA和SVM技术的方法，以降低SVM算法的复杂性。
