Diagnosing breast masses in digital mammography using feature selection and ensemble methods

Journal of Medical Systems

Volumn 36, Issue 2, 2012, Pages 569-577

  Luo, Shu Ting ^a   Cheng, Bor Wen ^a  

^a NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Breast cancer; Digital mammography; Ensemble classifiers; Feature selection

Indexed keywords

ARTICLE; BREAST CANCER; CLASSIFICATION ALGORITHM; DATA MINING; DECISION TREE; DIAGNOSTIC ACCURACY; DIAGNOSTIC IMAGING; DIAGNOSTIC TEST ACCURACY STUDY; DIGITAL MAMMOGRAPHY; ENSEMBLE CLASSIFICATION; FEATURE SELECTION; FEMALE; HUMAN; INTERMETHOD COMPARISON; MACHINE LEARNING; MAJOR CLINICAL STUDY; PREDICTION; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SEQUENTIAL MINIMAL OPTIMIZATION ALGORITHM; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; AGE; BREAST TUMOR; DECISION SUPPORT SYSTEM; IMAGE PROCESSING; MAMMOGRAPHY; METHODOLOGY; PREDICTIVE VALUE; RADIOGRAPHY;

AGE FACTORS; BREAST NEOPLASMS; DECISION SUPPORT SYSTEMS, CLINICAL; DECISION TREES; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAMMOGRAPHY; PREDICTIVE VALUE OF TESTS; SUPPORT VECTOR MACHINES;

EID: 84863220843     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9518-8     Document Type: Article

References (51)

1. Razavi, A. R., Gill, H., Åhlfeldt, H., and Shahsavar, N., Predicting metastasis in breast cancer: comparing a decision tree with domain experts. J. Med. Syst. 31:263-273, 2007. (Pubitemid 46988153)
2. Brenner, H., Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis. Lancet. 360:1131-1135, 2002. (Pubitemid 35246487)
3. Nystrom, L., Andersson, I., Bjurstam, N., Frisell, J., Nordenskjold, B., and Rutqvist, L. E., Long-term effects of mammography screening: updated overview of the Swedish randomised trials. Lancet. 359(9310):909-919, 2002. (Pubitemid 34246378)
4. Bjurstam, N., Bjorneld, L., Warwick, J., Sala, E., Duffy, S. W., Nyström, L., et al., The Gothenburg breast screening trial. Cancer. 97(10):2387-2396, 2003. (Pubitemid 36542476)
5. Rijnsburger, A. J., van Oortmarssen, G. J., Boer, R., Draisma, G., Miler, A. B., et al., Mammography benefit in the Canadian National Breast Screening Study-2: a model evaluation. Int. J. Cancer. 110 (5):756-762, 2004. (Pubitemid 38746035)
6. Carney, P. A., Miglioretti, D. L., Yankaskas, B. C., Kerlikowske, K., Rosenberg, R., Rutter, C. M., et al., Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann. Intern. Med. 138(3):168-175, 2003. (Pubitemid 36231622)
7. Pisano, E. D., Gatonis, C., Hendrick, E., Yaffe, M., Baum, J. K., Acharyya, S., et al., Diagnostic performance of digital versus film mammography for breast-cancer screening. N. Engl. J. Med. 353:1773-1783, 2005. (Pubitemid 41549889)
8. Pisano, E. D., Gatonis, C., Hendrick, E., Yaffe, M., Baum, J. K., Acharyya, S., et al., Diagnostic accuracy of digital versus film mammography: exploratory analysis of selected population subgroups in DMIST. Radiology. 246(3):376-383, 2008.
9. Kulkarni, A. D., Computer Vision and Fuzzy-Neural Systems. Prentice-Hall, Englewood-Cliffs, 2001.
10. Karssemeijer, N., Adaptive noise equalization and recognition of microcalcification clusters in mammograms. Int. J. Pattern. Recog. Artificial. Intell. 7(6):1357-1376, 1993.
11. Priebe, C. E., Lorey, R. A., Marchette, D. J., Solka, J. L., and Rogers, G. W., Nonparametric spatio-temporal change point analysis for early detection in mammography. In: Gale, A. G., Astley, S. M., Dance, D. R., and Cairns, A. Y. (Eds.), Digital mammography. Elsevier, Amsterdam, pp. 111-120, 1994.
12. Heine, J. J., Deans, S. R., Cullers, D. K., Stauduhar, R., and Clarke, L. P., Multiresolution statistical analysis of high-resolution digital mammograms. IEEE. Trans. Med. Imaging. 5(16):503-515, 1997. (Pubitemid 127767629)
13. Rakowski, W., and Clark, M. A., Do groups of women aged 50-75 match the national average mammography rate? Am. J. Prev. Med. 15(3):187-197, 1998. (Pubitemid 28443376)
14. Chhatwal, J., Alagoz, O., Lindstrom, M. J., Kahn, C. E., Jr., Shaffer, K. A., and Burnside, E. S., A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. Am. J. Roentgenol. 192(4):1117-1127, 2009.
15. Sameti, M., and Ward, R. K., A fuzzy segmentation algorithm for mammogram partitioning. In: Doi, K., Giger, M. L., Nishikawa, R. M., and Schmidt, R. A. (Eds.), Third international workshop on digital mammography. Elsevier, Amsterdam, pp. 471-474, 1996.
16. Qian, W., Sunden, P., Sjostrom, H., Fenger-Krog, H., and Brodin, U., Comparison of image quality for different digital mammogram image processing parameter settings versus analogue film. Electromedica. 71(1):2-6, 2003.
17. Verma, B., and Zakos, J. A., Computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE T. Inf. Technol. Biomed. 5(1):46-54, 2001. (Pubitemid 32302932)
18. Acharya, U. R., Ng, E. Y. K., Chang, Y. H., Yang, J., and Kaw, G. J. L., Computer-based identification of breast cancer sing digitized mammograms. J. Med. Syst. 32(6):499-507, 2008.
19. Rafayah, M., Qutaishat, M., and Abdallah, M., Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert. Syst. Appl. 28(4):713-723, 2005.
20. Verma, B., and Panchal, R., Neural networks for the classification of benign and malignant patterns in digital mammograms. In: Fulcher, J. (Ed.), Advances in applied artificial intelligence. Idea Group, USA, 2006.
21. Brijesh, B., Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms. Artif. Intell. Med. 42(1):67-79, 2008.
22. Li, Y., and Jiang, J., Combination of SVM knowledge for microcalcification detection in digital mammograms. Lect. Notes Comput. Sci. 3177:359-365, 2004.
23. de Oliveira Martins, L., Junior, G. B., Correa Silva, A., de Paiva, A. C., and Gattass, M., Detection of masses in digital mammograms using K-means and support vector machine. Electron. Lett. Comput. Vis. Image. Ana. 8(2):39-50, 2009.
24. Yang, J., and Olafsson, S., Optimization-based feature selection with adaptive instance sampling. Comput. Oper. Res. 33 (11):3088-3106, 2006. (Pubitemid 43333215)
25. Rodriguez, J. J., Kuncheva, L. I., and Alonso, C. J., Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28(10):1619-1630, 2006. (Pubitemid 46405081)
26. Kuncheva, L. I., Combining pattern classifiers: methods and algorithms. Wiley-IEEE Press, New York, 2004.
27. Schapire, R. E., The strength of weak learnability. Mach. Learn. 5 (2):197-227, 1990.
28. Mitchell, T., Machine learning. McGraw-Hill, New York, 1997.
29. Witten, I. H., and Frank, E., Data mining: practical machine learning tools with java implementations. Morgan Kaufmann, San Francisco, 2000.
30. Razavi, A.R., Gill, H., Åhlfeldt, H., and Shahsavar, N.: A data preprocessing method to increase efficiency and accuracy in data mining. In: Miksch, S., Hunter, J., Keravnou, E. (eds.) 10th Conference on Artificial Intelligence in Medicine. Springer-Verlag GmbH, Aberdeen, pp. 434-443, 2005.
31. Quinlan, J. R., C4.5: Programs for machine learning. CA: Morgan Kaufmann, San Mateo, 1993.
32. Vapnik, V. N., The nature of statistical learning theory. Springer, Berlin, 1995.
33. Platt, J.C.: Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report MSR-TR-98-14, Microsoft Research, 1998.
34. Melville, P., and Monney, R. J., Creating diversity in ensembles using artificial data. Inf. Fusion. 6(1):99-111, 2005.
35. Schapire, R. E., Freund, Y., Bartlett, P. L., and Lee, W. S., Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Statist. 26(5):1651-1686, 1998. (Pubitemid 128376902)
36. Breiman, L., Random forests. Mach. Learn. 45(1):5-32, 2001. (Pubitemid 32933532)
37. Kim, H. C., Pang, S., Je, H. M., Kim, D., and Bang, S. Y., Constructing support vector machine ensemble. Pattern. Recognit. 36(12):2757-2767, 2003. (Pubitemid 37293392)
38. Valentini, G., and Dietterich, T. G., Low bias bagged support vector machines. In: Fawcett, T., and Mishra, N. (Eds.), International conference on machine learning. AAAI press, California, 2003.
39. Breiman, L., Bagging predictors. Mach. Learn. 24(2):123-140, 1996. (Pubitemid 126724382)
40. Freund, Y., and Schapire, R. E., A decision-theoretic generalization of on-line learning and an application to Boosting. J. Comput. Syst. Sci. 55(1):119-139, 1997. (Pubitemid 127433398)
41. Zhang, C. X., Zhang, J. S., and Zhang, G. Y., An efficient modified Boosting method for solving classification problems. J. Comput. Appl. Math. 214(2):381-392, 2008.
42. Webb, G. I., MultiBoosting: a technique for combining Boosting and wagging. Mach. Learn. 40(2):159-197, 2000.
43. Delen, D., Walker, G., and Kadam, A., Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34:113-127, 2005. (Pubitemid 40719029)
44. Centor, R. M., Signal detectability: the use of ROC curves and their analyses. Med. Decis. Mak. 11:102-106, 1991.
45. Hanley, J. A., and McNeil, B., The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 143(1):29-36, 1982. (Pubitemid 12142173)
46. DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 44:837-845, 1988.
47. Newmann, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning database. http://archive.ics.uci.edu/ml/datasets/ Mammographic+Mass, Irvine, CA: University of California, Department of Information and Computer Science, (1998)
48. Kopans, D. B., D'Orsi, C. J., Adler, D. D., et al., Breast Imaging Reporting and Data System (BIRADS). American College of Radiology, Reston, 1993.
49. Elter, M., Wendtland, R. S., and Wittenberg, T., The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Med. Phys. 34 (11):4164-4172, 2007. (Pubitemid 350029638)
50. Zhang, G. P., Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 30(4):451-462, 2000. (Pubitemid 32190902)
51. Zangwill, L. M., Chan, K., Bowd, C., Hao, J., Lee, T. W., Weinreb, R. N., et al., Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers. Invest. Ophthalmol. Vis. Sci. 45(3):3144-3151, 2004. (Pubitemid 39121848)