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Proceedings Paper

Near-infrared Raman spectroscopy for in vivo diagnosis of cervical dysplasia: a probability-based multi-class diagnostic algorithm
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Paper Abstract

We report the development of a probability-based multi-class diagnostic algorithm to simultaneously distinguish highgrade dysplasia from low-grade dysplasia, squamous metaplasia as well as normal human cervical tissues using nearinfrared Raman spectra acquired in-vivo from the cervix of patients at the Vanderbilt University Medical Center. Extraction of diagnostic features from the Raman spectra uses the recently formulated theory of nonlinear Maximum Representation and Discrimination Feature (MRDF), and classification into respective tissue categories is based on the theory of Sparse Multinomial Logistic Regression (SMLR), a recent Bayesian machine-learning framework of statistical pattern recognition. The algorithm based on MRDF and SMLR was found to provide very good diagnostic performance with a predictive accuracy of ~90% based on leave-one-out cross validation in classifying the tissue Raman spectra into the four different classes, using histology as the "gold standard". The inherently multi-class nature of the algorithm facilitates a rapid and simultaneous classification of tissue spectra into various tissue categories without the need to train and heuristically combine multiple binary classifiers. Further, the probabilistic framework of the algorithm makes it possible to predict the posterior probability of class membership in discriminating the different tissue types.

Paper Details

Date Published: 6 February 2007
PDF: 11 pages
Proc. SPIE 6430, Advanced Biomedical and Clinical Diagnostic Systems V, 64300Q (6 February 2007); doi: 10.1117/12.724873
Show Author Affiliations
Shovan K. Majumder, Vanderbilt Univ. (United States)
Elizabeth Kanter, Vanderbilt Univ. (United States)
Amy Robichaux Viehoever, Vanderbilt Univ. (United States)
Howard Jones, Vanderbilt Univ. (United States)
Anita Mahadevan-Jansen, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 6430:
Advanced Biomedical and Clinical Diagnostic Systems V
Ramesh Raghavachari; Tuan Vo-Dinh; Warren S. Grundfest; David A. Benaron; Gerald E. Cohn, Editor(s)

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