Breast Tumor Classification Using Fast Convergence Recurrent Wavelet Elman Neural Networks

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.

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