Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis

Gopal Raghunath Karemore; Kim Komal Mascarenhas; Choudhary Patil; Unnikrishnan V.K; Vijendra Prabhu; Arunkumar Chowla; Mads Nielsen; Santhos C

doi:10.1109/BIBE.2008.4696752

Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis

Gopal Raghunath Karemore, Kim Komal Mascarenhas, Choudhary Patil, Unnikrishnan V.K, Vijendra Prabhu, Arunkumar Chowla, Mads Nielsen, Santhos C

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 language	English
Title of host publication	BIBE 2008 : 8th. IEEE International Conference on Bioinformatics and BioEngineering, 8-10 October 2008
Number of pages	6
Publisher	IEEE Communications Society
Publication date	2008
Pages	1-6
ISBN (Print)	978-1-4244-2844-1
DOIs	https://doi.org/10.1109/BIBE.2008.4696752
Publication status	Published - 2008
Event	IEEE International Conference on Bioinformatics and BioEngineering - Athens, Greece Duration: 8 Oct 2008 → 10 Oct 2008 Conference number: 8

Conference

Conference	IEEE International Conference on Bioinformatics and BioEngineering
Number	8
Country/Territory	Greece
City	Athens
Period	08/10/2008 → 10/10/2008

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10.1109/BIBE.2008.4696752

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Karemore, G. R., Mascarenhas, K. K., Patil, C., V.K, U., Prabhu, V., Chowla, A., Nielsen, M., & C, S. (2008). Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis. In BIBE 2008: 8th. IEEE International Conference on Bioinformatics and BioEngineering, 8-10 October 2008 (pp. 1-6). IEEE Communications Society. https://doi.org/10.1109/BIBE.2008.4696752

Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis. / Karemore, Gopal Raghunath; Mascarenhas, Kim Komal; Patil, Choudhary et al.
BIBE 2008: 8th. IEEE International Conference on Bioinformatics and BioEngineering, 8-10 October 2008. IEEE Communications Society, 2008. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Karemore, GR, Mascarenhas, KK, Patil, C, V.K, U, Prabhu, V, Chowla, A, Nielsen, M & C, S 2008, Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis. in BIBE 2008: 8th. IEEE International Conference on Bioinformatics and BioEngineering, 8-10 October 2008. IEEE Communications Society, pp. 1-6, IEEE International Conference on Bioinformatics and BioEngineering, Athens, Greece, 08/10/2008. https://doi.org/10.1109/BIBE.2008.4696752

Karemore GR, Mascarenhas KK, Patil C, V.K U, Prabhu V, Chowla A et al. Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis. In BIBE 2008: 8th. IEEE International Conference on Bioinformatics and BioEngineering, 8-10 October 2008. IEEE Communications Society. 2008. p. 1-6 doi: 10.1109/BIBE.2008.4696752

Karemore, Gopal Raghunath ; Mascarenhas, Kim Komal ; Patil, Choudhary et al. / Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis. BIBE 2008: 8th. IEEE International Conference on Bioinformatics and BioEngineering, 8-10 October 2008. IEEE Communications Society, 2008. pp. 1-6

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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.",

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AU - Patil, Choudhary

AU - V.K, Unnikrishnan

AU - Prabhu, Vijendra

AU - Chowla, Arunkumar

AU - Nielsen, Mads

AU - C, Santhos

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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.

AB - 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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Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis

Abstract

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