Automatic classification of prostate cancer using pseudo-gaussian radial basis function neural network
Olga Valenzuela, Ignacio Rojas, Fernando Rojas, Luisa Marquez · 2005
Abstract-Recent advances in multimedia and image processing techniques can be utilized to assist pathologists in this respect. In fact, many investigators believe that automation of prostate cancer analysis increases the rate of early detection. In this paper, we will propose an automatic procedure for prostate cancer light micrograph based on soft-computing technique, for image interpretation, with increased accuracy. We propose a feature subset selection algorithm that selects the most important features, used by a pseudo-gaussian radial basis function neural networks to classify the prostate cancer light micrograph. A high classification rate has been achieved which will reduce the subjective human invention and will increase the diagnostic speed. 1.