Performance comparison for MLP networks using various back propagation algorithms for breast cancer diagnosis

  • Authors:

  • S. Esugasini;Mohd Yusoff Mashor;Nor Ashidi Mat Isa;Nor Hayati Othman

  • Affiliations:

  • Control and Electronic Intelligent System (CELIS) Research Group, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang, Malaysia;Control and Electronic Intelligent System (CELIS) Research Group, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang, Malaysia;Control and Electronic Intelligent System (CELIS) Research Group, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang, Malaysia;School Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian, Kelantan, Malaysia

  • Venue:
  • KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
  • Year:
  • 2005

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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.