Principal Component Analysis (PCA)-Based k-Nearestm Neighbor (k-NN) Analysis of Colonic Mucosal Tissue Fluorescence Spectra

Kamath, Sudha D and Mahato, Krishna K (2009) Principal Component Analysis (PCA)-Based k-Nearestm Neighbor (k-NN) Analysis of Colonic Mucosal Tissue Fluorescence Spectra. Photomedicine and Laser Surgery, 27 (4). pp. 659-668.

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Objective: The objective of this study was to verify the suitability of principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis for discriminating normal and malignant autofluorescence spectra of colonic mucosal tissues. Background Data: Autofluorescence spectroscopy, a noninvasive technique, has high specificity and sensitivity for discrimination of diseased and nondiseased colonic tissues. Previously, we assessed the efficacy of the technique on colonic data using PCA Match=No match and Artificial Neural Networks (ANNs) analyses. To improve the classification reliability, the present work was conducted using PCA-based k-NN analysis and was compared with previously obtained results. Methods: A total of 115 fluorescence spectra (69 normal and 46 malignant) were recorded from 13 normal and 10 malignant colonic tissues with 325nm pulsed laser excitation in the spectral region 350–600nm in vitro. We applied PCA to extract the relevant information from the spectra and used a nonparametric k-NN analysis for classification. Results: The normal and malignant spectra showed large variations in shape and intensity. Statistically significant differences were found between normal and malignant classes. The performance of the analysis was evaluated by calculating the statistical parameters specificity and sensitivity, which were found to be 100% and 91.3%, respectively. Conclusion: The results obtained in this study showed good discrimination between normal and malignant conditions usingPCA-based k-NN analysis.

Item Type: Article
Subjects: Life Sciences > MLSC Manipal
Depositing User: KMC Manipal
Date Deposited: 08 Feb 2012 10:21
Last Modified: 07 Oct 2013 06:47

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