Improving Diagnosis of Cervical Pre-Cancer: Combination of PCA and SVM Applied on Fluorescence Lifetime Images

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.

Abstract licence: CC BY

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