Classification of breast masses in mammograms using genetic programming and feature selection

  • R. J. Nandi
  • A. K. Nandi
  • R. M. Rangayyan
  • D. Scutt
Original Article

Abstract

Mammography is a widely used screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. A small dataset of 57 breast mass images, each with 22 features computed, was used in this investigation; the same dataset has been previously used in other studies. The extracted features relate to edge-sharpness, shape, and texture. The novelty of this paper is the adaptation and application of the classification technique called genetic programming (GP), which possesses feature selection implicitly. To refine the pool of features available to the GP classifier, we used feature-selection methods, including the introduction of three statistical measures—Student’s t test, Kolmogorov–Smirnov test, and Kullback–Leibler divergence. Both the training and test accuracies obtained were high: above 99.5% for training and typically above 98% for test experiments. A leave-one-out experiment showed 97.3% success in the classification of benign masses and 95.0% success in the classification of malignant tumors. A shape feature known as fractional concavity was found to be the most important among those tested, since it was automatically selected by the GP classifier in almost every experiment.

Keywords

Pattern classification Breast masses Breast tumors Breast cancer Computer-aided diagnosis Genetic programming Feature selection 

Notes

Acknowledgments

This research work was partly funded by the Medical Research Council, UK, through the InterDisciplinary Bridging Awards (IDBA) scheme, and by a grant from the University of Calgary Research Grants Committee. Authors would like to thank Mr. L. Zhang, a research student at the University of Liverpool, for his initial assistance with genetic programming code.

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Copyright information

© International Federation for Medical and Biological Engineering 2006

Authors and Affiliations

  • R. J. Nandi
    • 1
  • A. K. Nandi
    • 1
  • R. M. Rangayyan
    • 2
  • D. Scutt
    • 3
  1. 1.Department of Electrical Engineering and ElectronicsThe University of LiverpoolBrownlow Hill, LiverpoolUK
  2. 2.Department of Electrical and Computer Engineering, Schulich School of EngineeringUniversity of CalgaryCalgaryCanada
  3. 3.School of Health SciencesThe University of LiverpoolLiverpoolUK

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