Application of Neural Networks for the Classi

Three dierent methods were investigated to determine their ability to detect and classify various categories of diuse liver disease. A statistical method, i.e., discriminant analysis, a supervised neural network called backpropagation and a nonsupervised, self-organizing feature map were examined. The investigation was performed on the basis of a previously selected set of acoustic and image texture parameters. The limited number of patients was successfully extended by generating additional but independent data with identical statistical properties. The generated data were used for training and test sets. The nal test was made with the original patient data as a validation set. It is concluded that neural networks are an attractive alternative to traditional statistical techniques when dealing with medical detection and classication tasks. Moreover, the use of generated data for training the networks and the discriminant classier has been shown to be justiied and prootable.