Title: Hybrid computing based intelligent system for breast cancer diagnosis

Authors: R.R. Janghel; Anupam Shukla; Ritu Tiwari

Addresses: Soft Computing and Expert System Laboratory, ABV-Indian Institute of Information Technology and Management, Gwalior 474010, India ' Soft Computing and Expert System Laboratory, ABV-Indian Institute of Information Technology and Management, Gwalior 474010, India ' Soft Computing and Expert System Laboratory, ABV-Indian Institute of Information Technology and Management, Gwalior 474010, India

Abstract: Breast cancer is one of the major causes of death in women which accounts one out of eight. As primary cause is still unknown, early detection increases better treatment and improves total recovery. We present some novel hybrid approaches for classification of breast cancer. Artificial Neural Network (ANN) which suffers credit assignment problem can be avoided by modular and evolutionary artificial neural network which achieves simple and small individual neural network. Ensemble of ANN is to obtain a more reliable and accurate ANN. Evolutionary Neural Network (ENN) is used for optimisation of neural network learning and design. The best accuracies achieved for diagnosis are around 99% using breast cancer datasets.

Keywords: ANNs; artificial neural networks; breast cancer; expert systems; MNNs; modular neural networks; ENNs; evolutionary neural networks; classification; hybrid intelligent diagnosis; biomedical engineering; cancer diagnosis; early detection.

DOI: 10.1504/IJBET.2012.049321

International Journal of Biomedical Engineering and Technology, 2012 Vol.10 No.1, pp.1 - 18

Published online: 12 Dec 2014 *

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