Breast cancer detection using cartesian genetic programming evolved artificial neural networks

Authors:
Arbab Masood Ahmad;Gul Muhammad Khan;Sahibzada Ali Mahmud;Julian Francis Miller
Affiliations:
UET Peshawar, Peshawar, Pakistan;University of Engineering and Technology Peshawar, Pakistan, Peshawar, Pakistan;UET Peshawar, Peshawar, Pakistan;University of York, UK, York, United Kingdom
Venue:
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Year:
2012

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Abstract

A fast learning neuro-evolutionary technique that evolves Artificial Neural Networks using Cartesian Genetic Programming (CGPANN) is used to detect the presence of breast cancer. Features from breast mass are extracted using fine needle aspiration (FNA) and are applied to the CGPANN for diagnosis of breast cancer. FNA data is obtained from the Wisconsin Diagnostic Breast Cancer website and is used for training and testing the network. The developed system produces fast and accurate results when compared to contemporary work done in the field. The error of the model comes out to be as low as 1% for Type-I (classifying benign sample falsely as malignant) and 0.5% for Type-II (classifying malignant sample falsely as benign).