Research Papers

Classification of in vivo autofluorescence spectra using support vector machines

[+] Author Affiliations
WuMei Lin, Xin Yuan

Hong Kong University of Science & Technology, Department of Electrical & Electronic Engineering, Clear Water Bay, Kowloon, Hong Kong, China

Powing Yuen, William I. Wei

University of Hong Kong, Queen Mary Hospital, Division of Otorhinolaryngology, Hong Kong

Jonathan Sham

University of Hong Kong, Department of Clinical Oncology, Hong Kong

PengCheng Shi, Jianan Qu

Hong Kong University of Science & Technology, Department of Electrical & Electronic Engineering, Clear Water Bay, Kowloon, Hong Kong, ChinaE-mail: eequ@ust.hk

J. Biomed. Opt. 9(1), 180-186 (Jan 01, 2004). doi:10.1117/1.1628244
History: Received Jan. 22, 2003; Revised May 19, 2003; Revised Jul. 10, 2003; Accepted Jul. 15, 2003; Online January 07, 2004
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An aglorithm 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. © 2004 Society of Photo-Optical Instrumentation Engineers.

© 2004 Society of Photo-Optical Instrumentation Engineers

Citation

WuMei Lin ; Xin Yuan ; Powing Yuen ; William I. Wei ; Jonathan Sham, et al.
"Classification of in vivo autofluorescence spectra using support vector machines", J. Biomed. Opt. 9(1), 180-186 (Jan 01, 2004). ; http://dx.doi.org/10.1117/1.1628244


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