Classification of laser induced fluorescence spectra from normal and malignant tissues using learning vector quantization neural network in bladder cancer diagnosis

Gopal Karemore, Kim Komal Mascarenhas, K. S. Choudhary, Ajeethkumar Patil, V. K. Unnikrishnan, Vijendra Prabhu, Arunkumar Chowla, Mads Nielsen, C. Santhosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In the present work we discuss the potential of recently developed classification algorithm, Learning Vector Quantization (LVQ), for the analysis of Laser Induced Fluorescence (LIF) Spectra, recorded from normal and malignant bladder tissue samples. The algorithm is prototype based and inherently regularizing, which is desirable, for the LIF spectra because of its high dimensionality and features being settled at widely spaced intervals (sparseness). We discuss the effect of different parameters influencing the performance of LVQ in LIF data classification. Further, we compare and cross validate the classification accuracy of LVQ with other classifiers (eg. SVM and Multi Layer Perceptron) for the same data set. Good agreement has been obtained between LVQ based classification of spectroscopy data and histopathology results which demonstrate the use of LVQ classifier in bladder cancer diagnosis.

Original languageEnglish
Title of host publication8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008
DOIs
Publication statusPublished - 01-12-2008
Event8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008 - Athens, Greece
Duration: 08-10-200810-10-2008

Conference

Conference8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008
CountryGreece
CityAthens
Period08-10-0810-10-08

Fingerprint

Vector quantization
Urinary Bladder Neoplasms
Lasers
Fluorescence
Learning
Tissue
Neural networks
Classifiers
Neural Networks (Computer)
Multilayer neural networks
Learning algorithms
Spectrum Analysis
Urinary Bladder
Spectroscopy

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering

Cite this

Karemore, Gopal ; Mascarenhas, Kim Komal ; Choudhary, K. S. ; Patil, Ajeethkumar ; Unnikrishnan, V. K. ; Prabhu, Vijendra ; Chowla, Arunkumar ; Nielsen, Mads ; Santhosh, C. / Classification of laser induced fluorescence spectra from normal and malignant tissues using learning vector quantization neural network in bladder cancer diagnosis. 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008. 2008.
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abstract = "In the present work we discuss the potential of recently developed classification algorithm, Learning Vector Quantization (LVQ), for the analysis of Laser Induced Fluorescence (LIF) Spectra, recorded from normal and malignant bladder tissue samples. The algorithm is prototype based and inherently regularizing, which is desirable, for the LIF spectra because of its high dimensionality and features being settled at widely spaced intervals (sparseness). We discuss the effect of different parameters influencing the performance of LVQ in LIF data classification. Further, we compare and cross validate the classification accuracy of LVQ with other classifiers (eg. SVM and Multi Layer Perceptron) for the same data set. Good agreement has been obtained between LVQ based classification of spectroscopy data and histopathology results which demonstrate the use of LVQ classifier in bladder cancer diagnosis.",
author = "Gopal Karemore and Mascarenhas, {Kim Komal} and Choudhary, {K. S.} and Ajeethkumar Patil and Unnikrishnan, {V. K.} and Vijendra Prabhu and Arunkumar Chowla and Mads Nielsen and C. Santhosh",
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Karemore, G, Mascarenhas, KK, Choudhary, KS, Patil, A, Unnikrishnan, VK, Prabhu, V, Chowla, A, Nielsen, M & Santhosh, C 2008, Classification of laser induced fluorescence spectra from normal and malignant tissues using learning vector quantization neural network in bladder cancer diagnosis. in 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008., 4696752, 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008, Athens, Greece, 08-10-08. https://doi.org/10.1109/BIBE.2008.4696752

Classification of laser induced fluorescence spectra from normal and malignant tissues using learning vector quantization neural network in bladder cancer diagnosis. / Karemore, Gopal; Mascarenhas, Kim Komal; Choudhary, K. S.; Patil, Ajeethkumar; Unnikrishnan, V. K.; Prabhu, Vijendra; Chowla, Arunkumar; Nielsen, Mads; Santhosh, C.

8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008. 2008. 4696752.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Patil, Ajeethkumar

AU - Unnikrishnan, V. K.

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AU - Nielsen, Mads

AU - Santhosh, C.

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N2 - In the present work we discuss the potential of recently developed classification algorithm, Learning Vector Quantization (LVQ), for the analysis of Laser Induced Fluorescence (LIF) Spectra, recorded from normal and malignant bladder tissue samples. The algorithm is prototype based and inherently regularizing, which is desirable, for the LIF spectra because of its high dimensionality and features being settled at widely spaced intervals (sparseness). We discuss the effect of different parameters influencing the performance of LVQ in LIF data classification. Further, we compare and cross validate the classification accuracy of LVQ with other classifiers (eg. SVM and Multi Layer Perceptron) for the same data set. Good agreement has been obtained between LVQ based classification of spectroscopy data and histopathology results which demonstrate the use of LVQ classifier in bladder cancer diagnosis.

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