Share Email Print

Journal of Biomedical Optics

Classification of in vivo autofluorescence spectra using support vector machines
Author(s): Wumei Lin; Xin Yuan; Po Wing Yuen; William I. Wei; Jonathan Sham; Peng-Cheng Shi; Jianan Y. Qu
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 1 January 2004
PDF: 7 pages
J. Biomed. Opt. 9(1) doi: 10.1117/1.1628244
Published in: Journal of Biomedical Optics Volume 9, Issue 1
Show Author Affiliations
Wumei Lin, Hong Kong Univ. of Science and Technology (Hong Kong China)
Xin Yuan, Hong Kong Univ. of Science and Technology (Hong Kong China)
Po Wing Yuen, Univ. of Hong Kong (Hong Kong China)
William I. Wei, Univ. of Hong Kong (Hong Kong China)
Jonathan Sham, Univ. of Hong Kong (Hong Kong China)
Peng-Cheng Shi, Hong Kong Univ. of Science and Technology (Hong Kong China)
Jianan Y. Qu, Hong Kong Univ. of Science and Technology (Hong Kong China)

© SPIE. Terms of Use
Back to Top