Artificial Evolution Methods in the Biological and Biomedical SciencesView this Special Issue
Research Article | Open Access
Conserved Self Pattern Recognition Algorithm with Novel Detection Strategy Applied to Breast Cancer Diagnosis
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.
- E. Marshall, “Search for a killer: focus shifts from fat to hormones.,” Science, vol. 259, no. 5095, pp. 618–621, 1993.
- 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.
- 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.
- 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.
- O. L. Mangasarian and W. H. Wolberg, “Cancer diagnosis via linear programming,” SIAM News, vol. 23, no. 5, pp. 1–18, 1990.
- 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.
- 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.
- 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.
- D. Dasgupta, “Advances in artificial immune systems,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 40–43, 2006.
- P. Matzinger, “The danger model: a renewed sense of self,” Science, vol. 296, no. 5566, pp. 301–305, 2002.
- 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.
- 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.
- 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.
- UCI Machine Learning Repository, Center for Machine Learning and Intelligent Systems, University of California, Irvine, Calif, USA, http://archive.ics.uci.edu/ml.
- D. Moore, Basic Practice of Statistics, W. H. Freeman, San Francisco, Calif, USA, 2006.
- 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.
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.