Pattern recognition algorithms for tissue diagnosis by near-infrared FT-Raman spectroscopy

S. Nie; Y. Li; D. C.B. Redd; N. T. Yu

Pattern recognition algorithms for tissue diagnosis by near-infrared FT-Raman spectroscopy

S. Nie, Y. Li, D. C.B. Redd, N. T. Yu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A preliminary study on near-infrared laser-excited Fourier-transform (FT)-Raman spectroscopy is presented. Several conventional pattern classification algorithms are evaluated for accuracy and effectiveness, including multivariate linear regression, principal component analysis, mean difference projection, decision plane analysis, Bayes decision theory, and principal peak ratio. Pattern recognition schemes based on neural networks are also tested, such as backpropagation and K-nearest neighbors methods. It is demonstrated that pattern recognition algorithms are useful in the diagnosis and classification of diseased and healthy tissues.

Original language	English (US)
Title of host publication	Proceedings of the Annual Conference on Engineering in Medicine and Biology
Publisher	Publ by IEEE
Pages	1747-1748
Number of pages	2
Edition	pt 4
ISBN (Print)	0780302168
State	Published - 1991
Externally published	Yes
Event	Proceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Orlando, FL, USA Duration: Oct 31 1991 → Nov 3 1991

Publication series

Name	Proceedings of the Annual Conference on Engineering in Medicine and Biology
Number	pt 4
Volume	13
ISSN (Print)	0589-1019

Other

Other	Proceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
City	Orlando, FL, USA
Period	10/31/91 → 11/3/91

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

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Pattern recognition algorithms for tissue diagnosis by near-infrared FT-Raman spectroscopy. / Nie, S.; Li, Y.; Redd, D. C.B. et al.
Proceedings of the Annual Conference on Engineering in Medicine and Biology. pt 4. ed. Publ by IEEE, 1991. p. 1747-1748 (Proceedings of the Annual Conference on Engineering in Medicine and Biology; Vol. 13, No. pt 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nie, S, Li, Y, Redd, DCB & Yu, NT 1991, Pattern recognition algorithms for tissue diagnosis by near-infrared FT-Raman spectroscopy. in Proceedings of the Annual Conference on Engineering in Medicine and Biology. pt 4 edn, Proceedings of the Annual Conference on Engineering in Medicine and Biology, no. pt 4, vol. 13, Publ by IEEE, pp. 1747-1748, Proceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, FL, USA, 10/31/91.

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title = "Pattern recognition algorithms for tissue diagnosis by near-infrared FT-Raman spectroscopy",

abstract = "A preliminary study on near-infrared laser-excited Fourier-transform (FT)-Raman spectroscopy is presented. Several conventional pattern classification algorithms are evaluated for accuracy and effectiveness, including multivariate linear regression, principal component analysis, mean difference projection, decision plane analysis, Bayes decision theory, and principal peak ratio. Pattern recognition schemes based on neural networks are also tested, such as backpropagation and K-nearest neighbors methods. It is demonstrated that pattern recognition algorithms are useful in the diagnosis and classification of diseased and healthy tissues.",

author = "S. Nie and Y. Li and Redd, {D. C.B.} and Yu, {N. T.}",

year = "1991",

language = "English (US)",

isbn = "0780302168",

series = "Proceedings of the Annual Conference on Engineering in Medicine and Biology",

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booktitle = "Proceedings of the Annual Conference on Engineering in Medicine and Biology",

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note = "Proceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 31-10-1991 Through 03-11-1991",

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Y1 - 1991

N2 - A preliminary study on near-infrared laser-excited Fourier-transform (FT)-Raman spectroscopy is presented. Several conventional pattern classification algorithms are evaluated for accuracy and effectiveness, including multivariate linear regression, principal component analysis, mean difference projection, decision plane analysis, Bayes decision theory, and principal peak ratio. Pattern recognition schemes based on neural networks are also tested, such as backpropagation and K-nearest neighbors methods. It is demonstrated that pattern recognition algorithms are useful in the diagnosis and classification of diseased and healthy tissues.

AB - A preliminary study on near-infrared laser-excited Fourier-transform (FT)-Raman spectroscopy is presented. Several conventional pattern classification algorithms are evaluated for accuracy and effectiveness, including multivariate linear regression, principal component analysis, mean difference projection, decision plane analysis, Bayes decision theory, and principal peak ratio. Pattern recognition schemes based on neural networks are also tested, such as backpropagation and K-nearest neighbors methods. It is demonstrated that pattern recognition algorithms are useful in the diagnosis and classification of diseased and healthy tissues.

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M3 - Conference contribution

AN - SCOPUS:0026296925

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T3 - Proceedings of the Annual Conference on Engineering in Medicine and Biology

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EP - 1748

BT - Proceedings of the Annual Conference on Engineering in Medicine and Biology

PB - Publ by IEEE

T2 - Proceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 31 October 1991 through 3 November 1991

ER -

Pattern recognition algorithms for tissue diagnosis by near-infrared FT-Raman spectroscopy

Abstract

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