Breast tumor computer-aided diagnosis using self-validating cerebellar model neural networks

Abstract

Breast cancer is becoming a leading cause of death among women in the world. However, it is confirmed that early detection and accurate diagnosis of this disease can ensure a long survival of the patients. This study proposes a self-validation cerebellar model articulation controller (SVCMAC) neural network which can yield high accuracy of predication and low false-negative rate for breast cancer diagnosis. With its self-validation unit, the SVCMAC neural network has higher classification accuracy than the conventional CMAC neural network. The parameters of the receptive-field basis function and the weights are all updated first by training data, and the most suitable parameters are then chosen through the self-validation algorithm to retrain the neural network for better performance. Experimental results provide evidence that the SVCMAC neural network has a higher classification accuracy when compared with the BP neural network, LVQ neural network and CMAC neural network.