Support vector machine for optical diagnosis of cancer

Majumder, S. K. ; Ghosh, N. ; Gupta, P. K. (2005) Support vector machine for optical diagnosis of cancer Journal of Biomedical Optics, 10 (2). 024034_1-024034_14. ISSN 1083-3668

PDF - Publisher Version

Official URL:

Related URL:


We report the application of a support vector machine (SVM) for the development of diagnostic algorithms for optical diagnosis of cancer. Both linear and nonlinear SVMs have been investigated for this purpose. We develop a methodology that makes use of SVM for both feature extraction and classification jointly by integrating the newly developed recursive feature elimination (RFE) in the framework of SVM. This leads to significantly improved classification results compared to those obtained when an independent feature extractor such as principal component analysis (PCA) is used. The integrated SVM-RFE approach is also found to outperform the classification results yielded by traditional Fisher's linear discriminant (FLD)-based algorithms. All the algorithms are developed using spectral data acquired in a clinical in vivo laser-induced fluorescence (LIF) spectroscopic study conducted on patients being screened for cancer of the oral cavity and normal volunteers. The best sensitivity and specificity values provided by the nonlinear SVM-RFE algorithm over the data sets investigated are 95 and 96% toward cancer for the training set data based on leave-one-out cross validation and 93 and 97% toward cancer for the independent validation set data. When tested on the spectral data of the uninvolved oral cavity sites from the patients it yielded a specificity of 85%.

Item Type:Article
Source:Copyright of this article belongs to International Society for Optical Engineering.
ID Code:22261
Deposited On:23 Nov 2010 08:36
Last Modified:17 May 2016 06:20

Repository Staff Only: item control page