Biomedical subjects

G A Rovithakis

Publications and source records attributed to G A Rovithakis.

3 recordsLinked to original sources

PubMed2004

A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification.

In this paper, a hybrid neural network/genetic algorithm technique is presented, aiming at designing a feature extractor that leads to highly separable classes in the feature space. The application upon which the system is built, is the identification of the state of human peripheral vascular tissue (i.e., normal, fibrous and calcified). The system is further tested on the classification of spectra measured from the cell nucleii in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. As advantages of the proposed technique we may encounter the algorithmic nature of the design procedure, the optimized classification results and the fact that the system performance is less dependent on the classifier type to be used.

Journal Article↗

PubMed2004

High-Order Neural Network structure selection for function approximation applications using Genetic Algorithms.

Neural network literature for function approximation is by now sufficiently rich. In its complete form, the problem entails both parametric (i.e., weights determination) and structural learning (i.e., structure selection). The majority of works deal with parametric uncertainty assuming knowledge of the appropriate neural structure. In this paper we present an algorithmic approach to determine the structure of High Order Neural Networks (HONNs), to solve function approximation problems. The method is based on a Genetic Algorithm (GA) and is equipped with a stable update law to guarantee parametric learning. Simulation results on an illustrative example highlight the performance and give some insight of the proposed approach.

Journal Article↗

PubMed2001

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.

Arteriosclerosis↗