Artificial neural networks for discriminating pathologic from normal peripheral vascular tissue

The identification of the state of human peripheral vascular tissue by using artificial neural networks is discussed in this paper. Two different laser emission lines (He-Cd, Ar+) are used to excite the chromophores of tissue samples. The fluorescence spectrum obtained, is passed through a nonlinear filter based on a high-order (HO) neural network neural network (NN) [HONN] whose weights are updated by stable learning laws, to perform feature extraction. The values of the feature vector reveal information regarding the tissue state. Then a classical multilayer perceptron is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered. Our method achieves not only the discrimination between normal and pathologic human tissue, but also the successful discrimination between the different types of pathologic tissue (fibrous, calcified). Furthermore, the small time needed to acquire and analyze the fluorescence spectra together with the high rates of success, proves our method very attractive for real-time applications.