Rank M-type radial basis functions network for medical image processing applications
José A. Moreno-Escobar, Francisco Javier Gallegos-Funes, Volodymyr I. Ponomaryov · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
In this paper we present the capability of the Rank M-Type Radial Basis Function (RMRBF) Neural Network in medical image processing applications. The proposed neural network uses the proposed RM-estimators in the scheme of radial basis function to train the neural network. The RMRBF-based training is less biased by the presence of outliers in the training set and was proved an accurate estimation of the implied probabilities. Other RBF based algorithms were compared with our approach in pdf estimation on the microcalcification detection in mammographic image analysis. From simulation results we observe that the RMRBF gives better estimation of the implied pdfs and has show better classification capabilities.