%0 Journal Article
%T Improving Diagnosis of Cervical Pre-Cancer: Combination of PCA and SVM Applied on Fluorescence Lifetime Images
%A Asima Pradhan
%A Chayanika Kala
%A Gyana Ranjan Sahoo
%A Kiran Pandey
%A Pankaj Singh
%J Photonics | An Open Access Journal from MDPI
%D 2018
%R https://doi.org/10.3390/photonics5040057
%X Abstract We report a significant improvement in the diagnosis of cervical cancer through a combined application of principal component analysis (PCA) and support vector machine (SVM) on the average fluorescence decay profile of Fluorescence Lifetime Images (FLI) of epithelial hyperplasia (EH) and CIN-I cervical tissue samples, obtained ex-vivo. The fast and slow components of double exponential fitted fluorescence lifetimes were found to be higher for EH compared to the lifetimes of CIN-I samples. Application of PCA to the average time-resolved fluorescence decay profiles showed that the 2nd PC, in combination with 1st PC, enhanced the discrimination between EH and CIN-I tissues. Fluorescence lifetime and PC scores were then classified separately by using SVM support vector machine to identify the two. On applying SVM to a combination of fluorescence lifetime and PC scores, diagnostic capability improved significantly. View Full-Tex
%K fluorescence lifetime
%K PCA
%K PC scores
%K SVM
%U https://www.mdpi.com/2304-6732/5/4/57