Thyroid and breast cancer disease diagnosis using fuzzy-neural networks

Abstract

In this paper a new hybrid structure in which Neural Network and Fuzzy Logic are combined is proposed and its algorithm is developed. Fuzzy-CSFNN, Fuzzy-MLP and Fuzzy-RBF structures are constituted, and their performances are compared. Conic Section Function Neural Network (CSFNN) unifies the propagation rules of the Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) networks at a unique network by its distinctive propagation rules. That means CSFNNs accommodate MLPs and RBFs in its own self-network structure. The proposed approach is implemented in a well-known benchmark medical problem with real clinical data for thyroid and breast cancer disease diagnosis. Simulation results show that proposed hybrid structures outperform both MATLAB-ANFIS and non-hybrid structures.