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Multi-class classification algorithm for optical diagnosis of oral cancer

Paper ID Volume ID Publish Year Pages File Format Full-Text
30567 44487 2006 9 PDF Available
Title
Multi-class classification algorithm for optical diagnosis of oral cancer
Abstract

We report development of a direct multi-class spectroscopic diagnostic algorithm for discrimination of high-grade cancerous tissue sites from low-grade as well as precancerous and normal squamous tissue sites of human oral cavity. The algorithm was developed making use of the recently formulated theory of total principal component regression (TPCR). The in vivo autofluorescence spectral data acquired from patients screened for neoplasm of oral cavity at the Government Cancer Hospital, Indore, was used to train and validate the algorithm. The diagnostic algorithm based on TPCR was found to provide satisfactory performance in classifying the tissue sites in four different classes – high-grade squamous cell carcinoma, low-grade squamous cell carcinoma, leukoplakia, and normal squamous tissue. The classification accuracy for these four classes was observed to be ∼94%, 100%, 100% and 91% for the training data set (based on leave-one-out cross-validation), and was ∼90%, 90%, 85% and 88%, respectively for the corresponding classes for the independent validation data set.

Keywords
Autofluorescence spectroscopy; Multi-class classification; Diagnostic algorithm; Leukoplakia; Oral cancer; Total principal component regression (TPCR); Squamous cell carcinoma (SCC)
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Publisher
Database: Elsevier - ScienceDirect
Journal: Journal of Photochemistry and Photobiology B: Biology - Volume 85, Issue 2, 1 November 2006, Pages 109–117
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us