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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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.
OriginalsprogEngelsk
TitelBIBE 2008 : 8th. IEEE International Conference on Bioinformatics and BioEngineering, 8-10 October 2008
Antal sider6
ForlagIEEE Communications Society
Publikationsdato2008
Sider1-6
ISBN (Trykt)978-1-4244-2844-1
DOI
StatusUdgivet - 2008
BegivenhedIEEE International Conference on Bioinformatics and BioEngineering - Athens, Grækenland
Varighed: 8 okt. 200810 okt. 2008
Konferencens nummer: 8

Konference

KonferenceIEEE International Conference on Bioinformatics and BioEngineering
Nummer8
LandGrækenland
ByAthens
Periode08/10/200810/10/2008

ID: 9746300