Share Email Print

Proceedings Paper

Classification of ENT tissue using near-infrared Raman spectroscopy and support vector machines
Author(s): Effendi Widjaja; Wei Zheng; Huang Zhiwei
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A recent developed pattern recognition algorithm, Support Vector Machines (SVM), was employed to classify nearinfrared Raman spectroscopy data collected from normal and cancerous ENT tissues. Three types of classifiers, linear, 3rd order polynomial, and radial basis function, were used. Highest diagnostic accuracy was obtained by 3rd order polynomial with a sensitivity of 91.86% and a specificity of 100%. The possibility to simplify SVM implementation was also explored by using principal component analysis (PCA) to extract significant principal components. It was found that the first five principal components as the data inputs were already sufficient to produce sensitivities of 100% and specificities of 100% for all these three classifiers. Combination PCA and linear discriminant analysis (LDA) to classify these ENT data was also performed and analysis results show that both methods, combination PCA & SVM and PCA & LDA yielded comparable performance.

Paper Details

Date Published: 7 October 2005
PDF: 6 pages
Proc. SPIE 5862, Diagnostic Optical Spectroscopy in Biomedicine III, 586205 (7 October 2005); doi: 10.1117/12.633007
Show Author Affiliations
Effendi Widjaja, Institute of Chemical & Engineering Sciences (Singapore)
Wei Zheng, National Univ. of Singapore (Singapore)
Huang Zhiwei, National Univ. of Singapore (Singapore)

Published in SPIE Proceedings Vol. 5862:
Diagnostic Optical Spectroscopy in Biomedicine III
Mary-Ann Mycek, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?