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Breast Tumor Classification Using Fast Convergence Recurrent Wavelet Elman Neural Networks

  • Enkh-Amgalan Boldbaatar
  • Lo-Yi Lin
  • Chih-Min LinEmail author
Article
  • 25 Downloads

Abstract

This paper develops an intelligent classification system for breast tumors that uses fine needle aspirate image data. A recurrent wavelet Elman neural network is used to classify the breast tumor as either benign or malignant. The structure of the RWENN uses different wavelet functions for hidden layers so that the generalization and search space are significantly greater than those of a conventional neural network. In this paper, there is also a stable convergence analysis of the RWENN classifier and the optimal learning rates are derived to guarantee the fastest convergence for the classification system. The performance of the developed classifier is compared with the Matlab neural network pattern recognition toolbox and other literature that uses a tenfold cross validation on the Wisconsin breast cancer dataset. The simulation results show that the proposed RWENN classifier has better classification results than other existing methods.

Keywords

Breast tumor classification Fine needle aspirate Recurrent wavelet Elman neural network Optimal learning rate 

Notes

Acknowledgements

The authors appreciate the financial support in part from the Nation Science Council of Republic of China under Grant NSC 101-2221-E-155-026-MY3.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Enkh-Amgalan Boldbaatar
    • 1
  • Lo-Yi Lin
    • 2
  • Chih-Min Lin
    • 3
    Email author
  1. 1.New Mongol Institute and TechnologyUlaanbaatarMongolia
  2. 2.Department of RadiologyTaipei Veterans General HospitalTaipeiTaiwan
  3. 3.Yuan Ze UniversityTaoyuanTaiwan

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