Hybrid computing based intelligent system for breast cancer diagnosis

Abstract

Breast cancer is one of the major causes of death in women which accounts one out of eight. As primary cause is still unknown, early detection increases better treatment and improves total recovery. We present some novel hybrid approaches for classification of breast cancer. Artificial Neural Network (ANN) which suffers credit assignment problem can be avoided by modular and evolutionary artificial neural network which achieves simple and small individual neural network. Ensemble of ANN is to obtain a more reliable and accurate ANN. Evolutionary Neural Network (ENN) is used for optimization of neural network learning and design. The best accuracies achieved for diagnosis are around 99% using breast cancer datasets.