USING CONSTRUCTIVE NEURAL NETWORKS FOR DETECTING CENTRAL VESTIBULAR SYSTEM LESION

Traditional neural network algorithms such as Backpropagation, require the definition of the network architecture prior to training. Generally these methods work well only when the network architecture is chosen appropriately. It is well known that there is no general answer to the problem of defining a neural network architecture for a given application. This fact has been one of the main motivations for the proposal of constructive neural network algorithms, which try to solve the problem by building the architecture of the neural network during its training. In this paper, we explore the use of three constructive supervised neural network algorithms in learning to identify the possibility of an existing problem in the central vestibular system of a patient, using data from optokinetic tests. The constructive neural network algorithms used are two perceptron-based algorithms, namely, Tower and Pyramid and the DistAl algorithm. The goal of the experiments, aside from creating a good classifier for detecting the presence of a central vestibular system lesion, is also to compare and contrast the performance of the recently proposed DistAl against two well-known constructive algorithms in a medical domain that has not been greatly explored with these types of experiments. In addition, results obtained with Backpropagation are presented for comparison.