Title: Comparison of the performance of linear multivariate analysis methods for normal and dyplasia tissues differentiation using autofluorescence spectroscopy
Authors: Chu, Shou Chia
Hsiao, Tzu-Chien Ryan
Lin, Jen K.
Wang, Chih-Yu
Chiang, Huihua Kenny
Department of Computer Science
Keywords: colorectal tissue;light-induced autofluorescence;multivariate linear regression;oral tissue;partial least squares;principal component analysis
Issue Date: 1-Nov-2006
Abstract: We compared the performance of three widely used linear multivariate methods for autofluorescence spectroscopic tissues differentiation. Principal component analysis (PCA), partial least squares (PLS), and multivariate linear regression (MVLR) were compared for differentiating at normal, tubular adenoma/epithelial dysplasia and cancer in colorectal and oral tissues. The methods' performances were evaluated by cross-validation analysis. The group-averaged predictive diagnostic accuracies were 85% (PCA), 90% (PLS), and 89% (MVLR) for colorectal tissues; 89% (PCA), 90% (PLS), and 90% (MVLR) for oral tissues. This study found that both PLS and MVLR achieved higher diagnostic results than did PCA.
URI: http://dx.doi.org/10.1109/TBME.2006.883643
ISSN: 0018-9294
DOI: 10.1109/TBME.2006.883643
Volume: 53
Issue: 11
Begin Page: 2265
End Page: 2273
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