Journal of Artificial Evolution and Applications

Journal of Artificial Evolution and Applications / 2009 / Article
Special Issue

Artificial Evolution Methods in the Biological and Biomedical Sciences

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Research Article | Open Access

Volume 2009 |Article ID 130498 | 12 pages | https://doi.org/10.1155/2009/130498

Conserved Self Pattern Recognition Algorithm with Novel Detection Strategy Applied to Breast Cancer Diagnosis

Academic Editor: Marylyn Ritchie
Received01 Oct 2008
Accepted22 May 2009
Published18 Aug 2009

Abstract

This paper presents a novel approach based on an improved Conserved Self Pattern Recognition Algorithm to analyze cytological characteristics of breast fine-needle aspirates (FNAs) for clinical breast cancer diagnosis. A novel detection strategy by coupling domain knowledge and randomized methods is proposed to resolve conflicts on anomaly detection between two types of detectors investigated in our earlier work on Conserved Self Pattern Recognition Algorithm (CSPRA). The improved CSPRA is applied to detect the malignant cases using clinical breast cancer data collected by Dr. Wolberg (1990), and the results are evaluated for performance measure (detection rate and false alarm rate). Results show that our approach has promising performance on breast cancer diagnosis and great potential in the area of clinical diagnosis. Effects of parameters setting in the CSPRA are discussed, and the experimental results are compared with the previous works.

References

  1. E. Marshall, “Search for a killer: focus shifts from fat to hormones.,” Science, vol. 259, no. 5095, pp. 618–621, 1993. View at: Google Scholar
  2. S. W. Fletcher, W. Black, R. Harris, B. K. Rimer, and S. Shapiro, “Report of the international workshop on screening for breast cancer,” Journal of the National Cancer Institute, vol. 85, no. 20, pp. 1644–1656, 1993. View at: Google Scholar
  3. R. W. M. Giard and J. Hermans, “The value of aspiration cytologic examination of the breast: a statistical review of the medical literature,” Cancer, vol. 69, no. 8, pp. 2104–2110, 1992. View at: Google Scholar
  4. W. H. Wolberg and O. L. Mangasarian, “Multisurface method of pattern separation for medical diagnosis applied to breast cytology,” Proceedings of the National Academy of Sciences of the United States of America, vol. 87, no. 23, pp. 9193–9196, 1990. View at: Publisher Site | Google Scholar
  5. O. L. Mangasarian and W. H. Wolberg, “Cancer diagnosis via linear programming,” SIAM News, vol. 23, no. 5, pp. 1–18, 1990. View at: Google Scholar
  6. H. A. Abbass, “An evolutionary artificial neural networks approach for breast cancer diagnosis,” Artificial Intelligence in Medicine, vol. 25, no. 3, pp. 265–281, 2002. View at: Publisher Site | Google Scholar
  7. I. Taha and J. Ghosh, “Characterization of the Wisconsin breast cancer database using a hybrid symbolic-connectionist system,” in Proceedings of the Intelligent Engineering Systems through Artificial Neural Networks Conference (ANNIE '96), St Louis, Mo, USA, November 1996. View at: Google Scholar
  8. R. S. Parpinelli, H. S. Lopes, and A. A. Freitas, “An ant colony based system for data mining: applications to medical data,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '01), pp. 791–798, San Francisco, Calif, USA, 2001. View at: Google Scholar
  9. D. Dasgupta, “Advances in artificial immune systems,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 40–43, 2006. View at: Publisher Site | Google Scholar
  10. P. Matzinger, “The danger model: a renewed sense of self,” Science, vol. 296, no. 5566, pp. 301–305, 2002. View at: Publisher Site | Google Scholar
  11. C. A. Janeway Jr., “Approaching the asymptote? Evolution and revolution in immunology,” in Cold Spring Harbor Symposia on Quantitative Biology, vol. 54, pp. 1–13, 1989. View at: Google Scholar
  12. S. Yu and D. Dasgupta, “Conserved self pattern recognition algorithm,” in Proceedings of the 7th International Conference in Artificial Immune System (ICARIS '08), vol. 5132, pp. 279–290, Phuket, Thailand, August 2008. View at: Publisher Site | Google Scholar
  13. S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri, “Self-nonself discrimination in a computer,” in Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, pp. 202–212, IEEE Computer Society Press, Los Alamitos, Calif, USA, 1994. View at: Google Scholar
  14. UCI Machine Learning Repository, Center for Machine Learning and Intelligent Systems, University of California, Irvine, Calif, USA, http://archive.ics.uci.edu/ml.
  15. D. Moore, Basic Practice of Statistics, W. H. Freeman, San Francisco, Calif, USA, 2006.
  16. Z. Ji, D. Dasgupta, Z. Yang, and H. Teng, “Analysis of dental images using artificial immune systems,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '06), pp. 528–535, Vancouver, Canada, July 2006. View at: Google Scholar

Copyright © 2009 Senhua Yu and Dipankar Dasgupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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