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

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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 publicationBIBE 2008 : 8th. IEEE International Conference on Bioinformatics and BioEngineering, 8-10 October 2008
Number of pages6
PublisherIEEE Communications Society
Publication date2008
ISBN (Print)978-1-4244-2844-1
Publication statusPublished - 2008
EventIEEE International Conference on Bioinformatics and BioEngineering - Athens, Greece
Duration: 8 Oct 200810 Oct 2008
Conference number: 8


ConferenceIEEE International Conference on Bioinformatics and BioEngineering

ID: 9746300