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Luo, L.-K., Huang, D.-F., Ye, L.-J., Zhou, Q.-F., Shao, G.-F. and Peng, H. (2011) Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8, 122-129.
http://dx.doi.org/10.1109/TCBB.2010.44

has been cited by the following article:

• TITLE: Classification Using Two Layer Neural Network Back Propagation Algorithm

  AUTHORS: K. A. Mohamed Junaid

  KEYWORDS: Breast Cancer, Back Propagation, Neural Network, Mammogram, Sonography

  JOURNAL NAME: Circuits and Systems, Vol.7 No.8, June 6, 2016

  ABSTRACT: Worldwide breast cancer is the most common form of cancer death occurring in 12.6% of women. This paper presents a cost effective approach to classify the normal, malignant and benign tumor using two layer neural network back propagation algorithm. Back propagation algorithm is used to train the neural network. Parallelization techniques speed up the computation process and as a result two layer neural networks outperform the previous work in terms of accuracy. Breast cancer tumor database used for the testing purpose is from the CIA machine learning repository. The highest accuracy of 97.12% is achieved using the two layer neural network back propagation algorithm.