Title: Diagnosis of breast cancer by modular evolutionary neural networks

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

Addresses: Soft Computing and Expert System Laboratory, Indian Institute of Information Technology and Management Gwalior, Morena Link Road, Gwalior 474010, Madhya Pradesh, India. ' Soft Computing and Expert System Laboratory, Indian Institute of Information Technology and Management Gwalior, Morena Link Road, Gwalior 474010, Madhya Pradesh, India. ' Soft Computing and Expert System Laboratory, Indian Institute of Information Technology and Management Gwalior, Morena Link Road, Gwalior 474010, Madhya Pradesh, India. ' Soft Computing and Expert System Laboratory, Indian Institute of Information Technology and Management Gwalior, Morena Link Road, Gwalior 474010, Madhya Pradesh, India

Abstract: We construct a mixture of experts model for medical diagnosis. Each of the experts is a complex modular neural network. The first modularity clusters the entire input space into a set of modules. The second modularity divides the number of attributes. Each cluster is a neural network that solves the problem. The individual neural networks are evolved using genetic algorithms, which optimise the architecture along with the weights and biases. The complete system is used for the diagnosis of breast cancer. Experimental results show that the proposed system outperforms the traditional simple and hybrid approaches.

Keywords: modular neural networks; ensembles; evolutionary neural networks; breast cancer; biomedical engineering; hybrid computing; artificial neural networks; ANNs; evolutionary algorithms; soft computing; medical diagnosis; cancer diagnosis; genetic algorithms.

DOI: 10.1504/IJBET.2011.043179

International Journal of Biomedical Engineering and Technology, 2011 Vol.7 No.2, pp.194 - 211

Published online: 21 Jan 2015 *

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