Application of support vector machines to breast cancer screening using mammogram and clinical history data

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5032 I, Issue , 2003, Pages 546-556

  Land Jr , Walker H ^a   McKee, Dan ^a   Velazquez, Roberto ^a   Wong, Lut ^a   Lo, Joseph Y ^b   Anderson, Frances ^c  

^a State University of New York   (United States)

^b DUKE UNIVERSITY   (United States)

^c Lourdes Hospital   (United States)

Author keywords

Breast Cancer Detection; Evolutionary Programming Adaptive Boosting Hybrid; Pattern classification; Support Vector Machines

Indexed keywords

LEARNING SYSTEMS; MAMMOGRAPHY; PATTERN RECOGNITION; TUMORS;

MAMMOGRAMS;

MEDICAL IMAGING;

EID: 0041874900     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.480235     Document Type: Conference Paper

References (17)

1. American Cancer Society, "Cancer Facts & Figures 2002," [On-line], http://www.cancer.org/eprise/main/docroot/STT/STT_0, 2002.
2. P. Strax, Make Sure that You Do Not Have Breast Cancer, St. Martin's, NY, 1989.
3. J.Y. Lo, J.A. Baker, P.J. Kornguth, J.D. Iglehart, and C.E. Floyd, "Predicting Breast Cancer Invasion with Artificial Neural Networks on the Basis of Mammographic Features," Radiology, 203, pp. 159-163, 1997.
4. J.Y. Lo, J.A. Baker, P.J. Kornguth, and C.E. Floyd, Jr., "Effect of Patient History Data on the Prediction of Breast Cancer from Mammographic Findings with Artificial Neural Networks," Academic Radiology, 6, pp. 10-15, 1999.
5. th World Conference on Systemics, Cybernetics and Informatics, 10, pp. 343-7, 2000.
6. W.H. Land, Jr., T. Masters, and J.Y. Lo, "Application of a New Evolutionary Programming/Adaptive Boosting Hybrid to Breast Cancer Diagnosis," IEEE Congress on Evolutionary Computation Proceedings, 2000.
7. N. Cristanini, and Shawe-Taylor, J., An Introduction to Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University, 2000.
8. S. Haykin, Neural Networks, Prentice Hall, New Jersey, 1999.
9. S. Gunn, Support Vector Machines for Classification and Regression, ISIS Technical Report, Image Speech and Intelligent Systems Group, University of Southampton, May 14, 1998.
10. V.N. Vapnik, The Nature of Statistical Learning Theory, Wiley, New York, 1995.
11. V.N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
12. C.J.C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, 2, issue 2, pp. 121-167, Kluwer, Boston, 1998.
13. J. Platt, "Fast training of Support Vector Machines Using Sequential Minimal Optimization", in B. Scholkopf, C.J.C. Burges, and A.J. Smola, Eds., Advances in Kernel Methods: Support Vector Learning, MIT Press, 1998.
14. E.A. Sickles, "Periodic Mammographic follow-up of Probably Benign Lesions; results in 3,184 consecutive cases", Radiology, 179, pp. 463-468, 1991.
15. J.L. Lo, M.K. Markey, J.A. Baker and C.A. Floyd, "Cross-Institutional Evaluation of BI-RADS Predictive Model for Mammographic Diagnosis of Breast Cancer", AJR, 178, Feb. 2002, pp. 457-463.
16. W.H. Land, Jr., T. Masters, J.Y. Lo, D.W. McKee and F.R. Anderson, "Performance analysis of evolutionary computation (EC)/Adaptive Boosting (AB) hybrids for breast cancer classification," Proceedings of the Combined 4th World Multiconference on Systemics, Cybernetics and Informatics (SCI2001) and the 6th International conference on Information Systems Analysis and Synthesis (ISAS2001), Volume X, Orlando, Florida, July, 22-25, pp. 461-466
17. W.H. Land, Jr., T. Masters, J.Y. Lo, D.W. McKee and F.R. Anderson, "New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data," Proceedings of the IEEE SMCia01 Mountain Workshop on Soft Computing in Industrial Applications, Blacksburg, VA, June 25-27, pp. 47-52.