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

TY - GEN

T1 - Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis

AU - Karemore, Gopal Raghunath

AU - Mascarenhas, Kim Komal

AU - Patil, Choudhary

AU - V.K, Unnikrishnan

AU - Prabhu, Vijendra

AU - Chowla, Arunkumar

AU - Nielsen, Mads

AU - C, Santhos

N1 - Conference code: 8

PY - 2008

Y1 - 2008

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.

U2 - 10.1109/BIBE.2008.4696752

DO - 10.1109/BIBE.2008.4696752

M3 - Article in proceedings

SN - 978-1-4244-2844-1

SP - 1

EP - 6

BT - BIBE 2008

PB - IEEE Communications Society

T2 - IEEE International Conference on Bioinformatics and BioEngineering

Y2 - 8 October 2008 through 10 October 2008

ER -