Breast Cancer Classification Using Neural Network Approach: Mlp and Rbf

The classification of breast cancer is a medical application that poses a great challenge for researchers and scientists. The use of learning machine and artificial intelligence techniques has revolutionized the process of diagnosis and prognosis of the breast cancer. The aim of our study is to propose an approach for breast cancer distinguishing between different classes of breast cancer. This approach is based on the Wisconsin Diagnostic and Prognostic Breast Cancer datasets for feature selection, and the classification of different types of breast cancer using neural network approach, and especially the multi layer perceptron MLP and the radial basis function RBF. The data set consists of nine features that represent the input layer to the neural network. The neural network will classify the input features into two classes of cancer type (benign and malignant). The proposed approach tested on the database, resulted in 97 % succession rate of classification using RBF neural network. Neural network approach and especially the RBF technique seems an efficient method for classification in medical applications and especially for the breast cancer