Comparison between Levenberg-Marquardt and Scaled Conjugate Gradient training algorithms for Breast Cancer Diagnosis using MLP

Computerized diagnostic tools have received significant attention over the past few decades, in order to assist medical practitioners in diagnosis of disease based on a variety of test results. It provides a fast and accurate method for diagnosis, particularly in cases where medical practitioners need to deal with difficult diagnosis problems. In this paper, we present an examination of two popular training algorithms (Levenberg-Marquardt and Scaled Conjugate Gradient) for Multilayer Perceptron (MLP) diagnosis of breast cancer tissues. We test the performance of the training algorithms using features extracted from the Wisconsin Breast Cancer Database (WBCD), a benchmark dataset that has been extensively used in literature for breast cancer diagnosis. Based on our results, we conclude that both algorithms were comparable in terms of accuracy and speed. However, the LM algorithm has shown slightly better advantage in terms of accuracy (as evidenced in the average training accuracy and MSE) and speed (as evidenced in the average training iterations) on the best MLP structure (with 10 hidden units).