Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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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. / Karemore, Gopal Raghunath; Mascarenhas, Kim Komal; Patil, Choudhary; V.K, Unnikrishnan; Prabhu, Vijendra; Chowla, Arunkumar; Nielsen, Mads; C, Santhos.
BIBE 2008: 8th. IEEE International Conference on Bioinformatics and BioEngineering, 8-10 October 2008. IEEE Communications Society, 2008. s. 1-6.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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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
Y2 - 8 October 2008 through 10 October 2008
ER -
ID: 9746300