Classification of Laser Induced Fluorescence spectra from normal and malignant tissues using Learning Vector Quantization neural network in bladder cancer diagnosis

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 perception) 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.