Performance Comparison for MLP Networks Using Various Back Propagation Algorithms for Breast Cancer Diagnosis

Abstract

This paper represents the performance comparison of the Multilayered Perceptron (MLP) networks using various back propagation (BP) algorithms for breast cancer diagnosis. The training algorithms used are gradient descent with momentum and adaptive learning, resilient back propagation, Quasi-Newton and Levenberg-Marquardt. The performances of these four algorithms are compared with the standard steepest descent back propagation algorithm. The current study investigates and compares the accuracy, sensitivity, specificity, false negative and false positive results of the selected four algorithms to train MLP networks. The Papinicolou image of breast cancer cells were captured via an image analyzer and thirteen morphological features were extracted to numerical scores. The feature scores are used as data sets to train the MLP network. The MLP network using the Levenberg-Marquardt algorithm displays the best performance for all the five measurement criteria’s (accuracy, specificity, sensitivity, true positive and true negative) at a lower number of hidden nodes.