Hybrid neurocomputing for breast cancer detection

Abstract

Breast cancer is one of the major tumor related cause of death in women. Various artificial intelligence techniques have been used to improve the diagnoses procedures and to aid the physician's efforts. In this paper we summa izerourp elimina y study to detect breast cancer using a Flexible Neural Tree (FNT), Neural Network (NN), Wavelet Neural Network (WNN) and thei ensemble combination. For the FNT model, a tree-structure based evolution a y algorithm and the Pa tide Swarm Optimization (PSO) a e used to find an optimal FNT. For the NN and WNN, the PSO is employed to optimize the free parameters. The performance of each approach is evaluated using the b east cancer data set. Simulation results show that the obtained FNT model has a fewer number of variables with educed number of input features and without significant education in the detection accuracy. The over all accuracy could be improved by using an ensemble approach by a voting method.