Improving the performance of machine learning classifiers for Breast Cancer diagnosis based on feature selection

2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014

Volumn , Issue , 2014, Pages 209-217

  Perez, Noel ^a   Guevara, Miguel A ^b   Silva, Augusto ^b   Ramos, Isabel ^c   Loureiro, Joana ^c  

^a UNIVERSITY OF PORTO   (Portugal)

^b UNIVERSITY OF AVEIRO   (Portugal)

^c Universidade do Porto   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMINERALIZATION; CLASSIFICATION (OF INFORMATION); DISEASES; FEATURE EXTRACTION; STATISTICAL TESTS;

BREAST CANCER CLASSIFICATIONS; BREAST CANCER DIAGNOSIS; BUILDING MACHINES; FEATURE SELECTION METHODS; FEEDING MACHINES; IMAGE-BASED FEATURES; RECEIVER OPERATING CHARACTERISTIC CURVES; SEARCH SPACES;

MACHINE LEARNING;

EID: 84912103556     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.15439/2014F249     Document Type: Conference Paper

References (50)

1. M. D. Althuis, J. M. Dozier, W. F. Anderson, S. S. Devesa, and L. A. Brinton, "Global trends in breast cancer incidence and mortality 1973-1997", Int. J. Epidemiol., vol. 34, pp. 405-412, April 1, 2005, http://dx.doi.org/10.1093/ije/dyh414.
2. F. Kamangar, G. M. Dores, and W. F. Anderson, "Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world", Journal of clinical oncology, vol. 24, pp. 2137-2150, 2006, http://dx.doi.org/10.1200/JCO.2005.05.2308.
3. J. Brown, S. Bryan, and R. Warren, "Mammography screening: an incremental cost effectiveness analysis of double versus single reading of mammograms", BMJ (Clinical research ed.), vol. 312, pp. 809-812, 1996, http://dx.doi.org/10.1136/bmj.312.7034.809.
4. Z. Huo, M. L. Giger, C. J. Vyborny, and C. E. Metz, "Breast cancer: effectiveness of computer-aided diagnosis observer study with independent database of mammograms", Radiology, vol. 224, pp. 560-8, Aug 2002, http://dx.doi.org/10.1148/radiol.2242010703.
5. L. Hadjiiski, H. P. Chan, B. Sahiner, M. A. Helvie, M. A. Roubidoux, C. Blane, et al., "Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study", Radiology, vol. 233, pp. 255-65, Oct 2004, http://dx.doi.org/10.1148/radiol.2331030432.
6. L. Hadjiiski, B. Sahiner, M. A. Helvie, H. P. Chan, M. A. Roubidoux, C. Paramagul, et al., "Breast masses: computer-aided diagnosis with serial mammograms", Radiology, vol. 240, pp. 343-56, Aug 2006, http://dx.doi.org/10.1148/radiol.2401042099.
7. K. Horsch, M. L. Giger, C. J. Vyborny, L. Lan, E. B. Mendelson, and R. E. Hendrick, "Classification of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set", Radiology, vol. 240, pp. 357-68, Aug 2006, http://dx.doi.org/10.1148/radiol.2401050208.
8. L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo, and J. M. Reinhardt, "Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system", Journal of Magnetic Resonance Imaging, vol. 25, pp. 89-95, 2007, http://dx.doi.org/10.1002/jmri.20794.
9. A. Lauria, M. E. Fantacci, U. Bottigli, P. Delogu, F. Fauci, B. Golosio, et al., "Diagnostic performance of radiologists with and without different CAD systems for mammography", in Medical Imaging 2003, 2003, pp. 51-56, http://dx.doi.org/10.1117/12.480079.
10. E. D. Pisano, C. Gatsonis, E. Hendrick, M. Yaffe, J. K. Baum, S. Acharyya, et al., "Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening", N Engl J Med, vol. 353, pp. 1773-1783, October 27, 2005, http://dx.doi.org/10.1056/NEJMoa052911.
11. S. Ciatto, N. Houssami, D. Gur, R. M. Nishikawa, R. A. Schmidt, C. E. Metz, et al., "Computer-Aided Screening Mammography", N Engl J Med, vol. 357, pp. 83-85, July 5, 2007, http://dx.doi.org/10.1056/NEJMc071248.
12. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection", J. Mach. Learn. Res., vol. 3, pp. 1157-1182, 2003.
13. I. Guyon and A. Elisseeff, "An Introduction to Feature Extraction", in Feature Extraction. vol. 207, I. Guyon, M. Nikravesh, S. Gunn, and L. Zadeh, Eds., ed: Springer Berlin Heidelberg, 2006, pp. 1-25, http://dx.doi.org/10.1007/978-3-540-35488-8-1.
14. N. Pérez, M. A. Guevara, and A. Silva, "Improving breast cancer classification with mammography, supported on an appropriate variable selection analysis", in SPIE Medical Imaging, 2013, pp. 867022-867022-14, http://dx.doi.org/10.1117/12.2007912.
15. N. Pérez, M. A. Guevara, and A. Silva, "EVALUATION OF FEATURES SELECTION METHODS FOR BREAST CANCER CLASSIFICATION", Icem15: 15th International Conference on Experimental Mechanics, p. 10, 2012.
16. M. Dash and H. Liu, "Feature Selection for Classification", Intelligent Data Analysis, vol. 1, pp. 131-156, Jan 1, 1997.
17. H. Liu and R. Setiono, "Chi2: Feature Selection and Discretization of Numeric Attributes", 1995, pp. 388-388, http://dx.doi.org/10.1109/TAI.1995.479783.
18. B. P. Flannery, W. H. Press, S. A. Teukolsky, and W. Vetterling, "Numerical recipes in C", Press Syndicate of the University of Cambridge, New York, 1992.
19. R. Holte, "Very Simple Classification Rules Perform Well on Most Commonly Used Datasets", Machine Learning, vol. 11, pp. 63-90, 1993/04/01 1993, http://dx.doi.org/10.1023/A:1022631118932.
20. K. Kira and L. A. Rendell, "A practical approach to feature selection", presented at the Proceedings of the ninth international workshop on Machine learning, Aberdeen, Scotland, United Kingdom, 1992.
21. N. Pérez, M. A. Guevara, A. Silva, and I. Ramos, "Ensemble features selection method as tool for Breast Cancer classification", Computing and Informatics, 2013, unpublished (under review).
22. Y. H. Hu and J.-N. Hwang, "Introduction to Neural Networks for Signal Processing", in Handbook of neural network signal processing, ed: CRC press, 2001.
23. A. Papadopoulos, D. I. Fotiadis, and A. Likas, "Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines", Artificial Intelligence in Medicine, vol. 34, pp. 141-150, Jun 2005, http://dx.doi.org/10.1016/j.artmed.2004.10.001.
24. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification (2nd Edition): Wiley-Interscience, 2000.
25. M. A. Alolfe, A. M. Youssef, Y. M. Kadah, and A. S. Mohamed, "Computer-Aided Diagnostic System based on Wavelet Analysis for Microcalcification Detection in Digital Mammograms", in Biomedical Engineering Conference, 2008. CIBEC 2008. Cairo International, 2008, pp. 1-5, http://dx.doi.org/10.1109/CIBEC.2008.4786080.
26. S. Wang and R. M. Summers, "Machine learning and radiology", Medical Image Analysis, vol. 16, pp. 933-951, 2012, http://dx.doi.org/10.1016/j.media.2012.02.005.
27. J. Demšar, "Statistical comparisons of classifiers over multiple data sets", The Journal of Machine Learning Research, vol. 7, pp. 1-30, 2006.
28. M. Hollander and D. A. Wolfe, Nonparametric statistical methods, 2nd Edition ed.: Wiley-Interscience, 1999.
29. R. Ramos-Pollan, M. A. Guevara-Lopez, C. Suarez-Ortega, G. Diaz-Herrero, J. M. Franco-Valiente, M. Rubio-Del-Solar, et al., "Discovering mammography-based machine learning classifiers for breast cancer diagnosis", J Med Syst, vol. 36, pp. 2259-69, Aug 2012, http://dx.doi.org/10.1007/s10916-011-9693-2.
30. J. E. de Oliveira, A. M. Machado, G. C. Chavez, A. P. Lopes, T. M. Deserno, and A. Araujo Ade, "MammoSys: A contentbased image retrieval system using breast density patterns", Comput Methods Programs Biomed, vol. 99, pp. 289-97, Sep 2010, http://dx.doi.org/10.1016/j.cmpb.2010.01.005.
31. Júlia E. E. Oliveira, Mark O. Gueld, Arnaldo de A. Araújo, Bastian Ott, and T. M. Deserno., "Towards a Standard Reference Database for Computer-aided Mammography", in SPIE-Medical Imaging 2008: Computer-Aided Diagnosis, 69151Y, 2008, http://dx.doi.org/10.1117/12.770325.
32. H. Soltanian-Zadeh, F. Rafiee-Rad, and S. Pourabdollah-Nejad D, "Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms", Pattern Recognition, vol. 37, pp. 1973-1986, Oct 2004, http://dx.doi.org/10.1016/j.patcog.2003.03.001.
33. S.-K. Lee, P.-c. Chung, C.-I. Chang, C.-S. Lo, T. Lee, G.-C. Hsu, et al., "Classification of clustered microcalcifications using a Shape Cognitron neural network", Neural Networks, vol. 16, pp. 121-132, Jan 2003, http://dx.doi.org/10.1016/S0893-6080(02)00164-8.
34. Y. López, Novoa, Andra., Guevara, Miguel., Silva, Augusto, "Breast Cancer Diagnosis Based on a Suitable Combination of Deformable Models and Artificial Neural Networks Techniques", in Progress in Pattern Recognition, Image Analysis and Applications. vol. Volume 4756/2008, ed: Springer Berlin /Heidelberg, 2008, pp. 803-811, http://dx.doi.org/10.1007/978-3-540-76725-1-83.
35. Y. López, Novoa, Andra., Guevara, Miguel., Quintana, Nicolás., Silva, Augusto, "Computer Aided Diagnosis System to Detect Breast Cancer Pathological Lesions", in Progress in Pattern Recognition, Image Analysis and Applications. vol. Volume 5197/2008, ed: Springer Berlin /Heidelberg, 2008, pp. 453-460, http://dx.doi.org/10.1007/978-3-540-85920-8-56.
36. L. Talavera, "An Evaluation of Filter and Wrapper Methods for Feature Selection in Categorical Clustering", in Advances in Intelligent Data Analysis VI. vol. 3646, A. F. Famili, J. Kok, J. Peña, A. Siebes, and A. Feelders, Eds., ed: Springer Berlin Heidelberg, 2005, pp. 440-451, http://dx.doi.org/10.1007/11552253-40.
37. M. Elter and A. Horsch, "CADx of mammographic masses and clustered microcalcifications: a review", Medical physics, vol. 36, pp. 2052-2068, 2009, http://dx.doi.org/10.1118/1.3121511.
38. Z. Ping, B. Verma, and K. Kuldeep, "A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography", in Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on, 2004, pp. 2303-2308 vol.3, http://dx.doi.org/10.1109/IJCNN.2004.1380985.
39. M. E. Mavroforakis, H. V. Georgiou, N. Dimitropoulos, D. Cavouras, and S. Theodoridis, "Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers", Artif Intell Med, vol. 37, pp. 145-62, Jun 2006, http://dx.doi.org/10.1016/j.artmed.2006.03.002.
40. J. C. Fu, S. K. Lee, S. T. C. Wong, J. Y. Yeh, A. H. Wang, and H. K. Wu, "Image segmentation feature selection and pattern classification for mammographic microcalcifications", Computerized Medical Imaging and Graphics, vol. 29, pp. 419-429, Sep 2005, http://dx.doi.org/10.1016/j.compmedimag.2005.03.002.
41. J. Shi, B. Sahiner, H. P. Chan, J. Ge, L. Hadjiiski, M. A. Helvie, et al., "Characterization of mammographic masses based on level set segmentation with new image features and patient information", Med Phys, vol. 35, pp. 280-90, Jan 2008.
42. J. L. Jesneck, J. Y. Lo, and J. A. Baker, "Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors", Radiology, vol. 244, pp. 390-8, Aug 2007, http://dx.doi.org/10.1148/radiol.2442060712.
43. D. Moura and M. Guevara López, "An evaluation of image descriptors combined with clinical data for breast cancer diagnosis", International Journal of Computer Assisted Radiology and Surgery, vol. 8, pp. 561-574, Jul 2013, http://dx.doi.org/10.1007/s11548-013-0838-2.
44. G. I. Salama, M. Abdelhalim, and M. A.-e. Zeid, "Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers", Breast Cancer (WDBC), vol. 32, p. 2, 2012, .
45. A. Christobel, "An Empirical Comparison of Data mining Classification Methods", International Journal of Computer Information Systems, vol. 3, 2011.
46. F. García López, M. García Torres, B. Melián Batista, J. A. Moreno Pérez, and J. M. Moreno-Vega, "Solving feature subset selection problem by a parallel scatter search", European Journal of Operational Research, vol. 169, pp. 477-489, 2006, http://dx.doi.org/10.1016/j.ejor.2004.08.010.
47. T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, "The elements of statistical learning: data mining, inference and prediction", The Mathematical Intelligencer, vol. 27, pp. 83-85, 2005.
48. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: an update", ACM SIGKDD explorations newsletter, vol. 11, pp. 10-18, 2009, http://dx.doi.org/10.1145/1656274.1656278.
49. "American College of Radiology (ACR) ACR BIRADS-Mammography", in ACR Breast Imaging Reporting and Data System, Breast Imaging Atlas, Reston, VA, 2003.
50. R. M. Haralick, Shanmuga. K, and I. Dinstein, "Textural Features for Image Classification", IEEE Transactions on Systems Man and Cybernetics, vol. Smc3, pp. 610-621, 1973, http://dx.doi.org/10.1109/Tsmc.1973.4309314.