Artificial neural networks for feature extraction and classification of vascular tissue fluorescence spectrums

Abstract

The use of neural network structures for feature extraction and classification is addressed here. More precisely, a nonlinear filter based on higher order neural networks (HONN) whose weights are updated by stable learning laws is used to extract the characteristic features of fluorescence spectra corresponding to human tissue samples of different states. The features are then classified with a multi-layer perceptron (MLP). The high rates of success together with the small time needed to analyze the signals, proves our method very attractive for real time applications.