Expert system for breast cancer diagnosis using ensemble approach
DOI:
https://doi.org/10.30732/ijbbb.20160101001Keywords:
Breast Cancer, Medical Diagnostics, Pattern Recognition, Ensemble Approach, Neural NetworksAbstract
Development of efficient, prompt and robust systems that are intelligent enough to replace/reduce human supervision in medical diagnosis is one of the primary objectives that have driven advancements in research for long. One area where this need for intelligent automation has been most acutely felt is related to the diagnosis of breast cancer in women. Mammography is the most widely used test for screening and early diagnosis of breast cancer. However, it is error-prone and hence cannot be used reliably for effective diagnosis of the said disease. In this paper, we describe how the above said problem could be efficiently solved through Ensemble Approach. The use of the approach has bestowed the expert system with significantly simple and swift learning, smaller requirement for storage space during classification, faster classification with added possibility of incremental learning. The system was comparatively evaluated using different ensemble integration methods for breast cancer diagnosis namely weighted averaging, product, minimum, maximum, poling and fuzzy integration and different neural network approaches including MLP neural network, Radial Basis Function Network, Learning Vector Quantization and Recurrent Neural Network. Detailed experimental analysis with the system shows that the best performance in terms of accuracy and specificity measures is achieved while used maximum integration technique with Radial Bass Function Network while finest performance in terms of sensitivity is achieved when MLP neural network with Minimum integration is used.
References
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12. J. Amores and P. Radeva, Registration and retrieval of highly elastic bodies using contextual information. Pattern Recognition Letter 26(11):1720–1731, 2005
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14. V. Zapater, L. Martinez-Costa and G. Ayala, Classifying human endothelial cells based on individual granulometric size distributions. Image Vis Comput 20(11):783–791, 2002.
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28. R. Jain, A. Abraham, A Comparative Study of Fuzzy Classification Methods on Breast Cancer Data, 7th International Work Conference on Artificial and Natural Neural Networks, IWANN’03, Spain, 2003
29. H.D. Cheng, A neural network for breast cancer detection using fuzzy entropy approach, 1995 International Conference on Image Processing (ICIP'95) - Volume 3, 1995.
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32. L. K. Hansen and P. Salamon, Neural Network Ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. 12, No. 10, pp. 993-1001, 1990
33. J. Sharkey, On combining artificial neural networks. Connection Science, 8(3& 4):299–313, 1996.
34. L. Xu, A. Krzyzak, and C. Y. Suen, Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics 22:418–435, 1992.
35. N. J. Nilsson, Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw Hill, New York, 1965.
36. R. Clemen, Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, pp: 559– 583, 1989.
37. J. A. Barnett, Computational methods for a mathematical theory of evidence. In proceedings of IFCAI, pp: 868–875, 1981.
38. C. W. J. Granger, Combining forecasts-twenty years later, International Journal of Forecasting, pp: 8, 1989.
39. S. Haykins, Neural Networks, A comprehensive Foundation. Macmillan College Publishing Company, 1994.
40. R. Jacobs, M. Jordan and G. Hinton, Adaptive mixture of local experts, Neural Computation, 3(1):79–87, 1991.
41. C. Bauckhage and C. Thurau, Towards a fair’n square aimbot—Using mixture of experts to learn context aware weapon handling, in: Proccedings of GAME–ON, Ghent, Belgium, 2004, pp. 20–24.
42. P. Hartono and S. Hashimoto, Ensemble of linear perceptrons with confidence level output, Proceedings of 4th Intl. Conf. on Hybrid Intelligent Systems, 2004, pp. 186–191, 2004.
43. Verikas, A. Lipnickas, K. Malmqvist, M. Bacauskiene and A. Gelzinis, Pattern Recognition Letters 20 (1999), pp: 429—444, 1999.
44. R. A. Jacobs. Methods of combining experts’ probability assessments. Neural Computation, 7: 867–888, 1995.
45. O. Castillo and P. Melin, Soft Computing for Control of Non-Linear Dynamical Systems. Springer, Heidelberg (2001).
46. B. Verma, P. Mcleod and A. Klevansky, Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer, Expert Systems with Applications, Elsevier,Volume 37, Issue 4, pp: 3344-3351, 2010.
47. S. J. Kuo, Y. H. Hsiao, Y. L. Huang and D. R. Chen, Classification of benign and malignant breast tumors using neural networks and three-dimensional power Doppler ultrasound, Ultrasound in Obstetrics and Gynecology, Volume 32, Issue 1, pp: 97–102, 2008.
48. M. Karabatak and M. Cevdet Ince, An expert system for detection of breast cancer based on association rules and neural network, Expert Systems with Applications, Elsevier,Volume 36, Issue 2, Part 2, pp: 3465-3469, 2009.
49. C. M. M. Pareira and R. F. De Mello, A Radial Basis Function Neural Network approach to detect novelties: Applications on health datasets, Proceedings of the Joint Conferences on Pervasive Computing (JCPC), pp: 143 – 148, 2009.
50. V. Del Mar-Valparaiso, Breast cancer diagnosis based on a suitable combination of deformable models and artificial neural networks techniques, Proceedings of the Congress on pattern recognition 12th Ibero-american conference on Progress in pattern recognition, image analysis and applications, Lecture Notes In Computer Science, pp: 803-811, 2007.
51. R. R. Janghel, A. Shukla, R. Tiwari and R. Kala, Intelligent Decision Support System for Breast Cancer, Advances in Swarm Intelligence, Lecture Notes in Computer Science, Volume 6146/2010, 2010.
52. E. d. Ubeyli, Comparison of different classification algorithms in clinical decision-making, Expert Systems, Wiley, Volume 24, Issue 1, pp: 17–31, 2007.
53. E. d. Ubeyli, Implementing automated diagnostic systems for breast cancer detection, Expert Systems with Applications, Elsevier, Volume 33, Issue 4, pp: 1054-1062, 2007.
54. T. Kuyan and T. Yildirim, Breast cancer diagnosis using statistical neural networks, Journal of Electrical & Electronics Engineering, Volume: 4, Number: 2, 2004.
2. S. Watanabe, Pattern Recognition: Human and Mechanical, Wiley, 1985.
3. S. I. Weiss, and C. Kulikowski, Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Networks, Machine Learning and Expert Systems, Morgan Kaufmann Publishers, 1991.
4. J. Han, and M. Kamber, Data Mining: Concepts and Techniques, 2nd edition, Morgan Kaufmann Publishers, 2008.
5. D. Paulus, and J. Hornegger, Applied Pattern Recognition, 2nd edition, Morgan Kaufmann Publishers, 1998.
6. Anke Meyer Base, “Pattern Recognition for Medical Imagingâ€, Elsevier Publication, 2004.
7. Thomas Frederick Coleman, Yuying Li, Large Scale Numerical optimization, Proceedings of workshop on Large Scale Numerical optimization, Cornell University, New York, 1989.
8. www.breast cancer.org.
9. V.F. Andolina, S.L. Lille, and K.M. Willison, Mammographic Imaging: A Practical Guide. New York, NY: Lippincott Williams & Wilkins, 1992.
10. S. Saheb Basha and K. Satya Prasad , automatic detection of breast cancer mass in mammograms using morphological operators and fuzzy c –means clustering, Journal of Theoretical and Applied Information Technology, pp: 704-709.
11. L. Bocchi and J. Nori, Shape analysis of microcalcifications using Radon transform. Med Eng Phys 29(6):691–698, 2007.
12. J. Amores and P. Radeva, Registration and retrieval of highly elastic bodies using contextual information. Pattern Recognition Letter 26(11):1720–1731, 2005
13. Sameer Antani, D. J. Lee , L. R. Long and G. R. Thoma, Evaluation of shape similarity measurement methods for spine X-ray images. J Vis Commun Image Represent 15(3):285–302, 2004.
14. V. Zapater, L. Martinez-Costa and G. Ayala, Classifying human endothelial cells based on individual granulometric size distributions. Image Vis Comput 20(11):783–791, 2002.
15. B. Adam, S. J. Nover, W. Anjum, H. Yegingil, W. Y. Shih, W. Shih, and A. D. Brooks, International Journal of Biomedical Imaging, Volume 2009, pages 1-14, 2009.
16. N. K. Doshi, R.W. Silverman, Y. Shao and S.R. Cherry, MaxPET: a dedicated mammary and axillary region positron emission tomography imaging system for breast cancerâ€, Nuclear Science Symposium Conference Record, 20 IEEE, Volume: 3, pp.17, 2000.
17. S. L. Olson, B. W. Fam, P. F. Winter, F. J. Scholz, A. K. Lee, and S. E. Gorden, Breast calcifications: Analysis of imaging properties, Radiol., vol. 169, no. 2, pp. 329–332, 1988.
18. R. N. Strickland and H. I. Hahn, “Detection of micro calcifications using wavelets,†in Digital Mammography: Proceedings of the 2nd International Workshop on Digital Mammography, Elsevier Science, pp. 10–12, 1994.
19. L. Shen, R. M. Rangayyan, and J. E. L. Desautels, “Detection and classification of mammographic calcifications,†in State of the Art in Digital Mammographic Image Analysis, World Scientific, 1994.
20. Y. Wu and K. Doi, “Computerized detection of clustered microcalcifications in digital mammograms: Applications of artificial neural networks,†Med. Phys., vol. 19, no. 3, 1992.
21. P. Melin, A. Mancilla, M. Lopez and O. Mendoza, A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting, Applied Soft Computing August (7) (4) (2007) 1217–1226, 2007.
22. B. Adam, S. J. Nover, W. Anjum, H. Yegingil, W. Y. Shih, W. Shih, and A. D. Brooks†International Journal of Biomedical Imaging†Volume 2009, pages 1-14, 2009
23. G. D. Tourassi , M. K. Markey, J. Y. Lo and C. E. Floyd Jr., A neural network approach to breast cancer diagnosis as a constraint satisfaction problem, Medical Physics, May 2001;28(5):804-11, 2001.
24. S. D. Sawarkar, A. A. Ghatol, A. P. Pande, Neural network aided breast cancer detection and diagnosis, Proceedings of the 7th WSEAS International Conference on Neural Networks, Pages: 158-163 , 2006.
25. V. N. Chunekar, H. P. Ambulgekar, Approach of Neural Network to Diagnose Breast Cancer on three different Data Set, International Conference Advances in Recent Technologies in Communication and Computing, 2009. (ARTCom '09), pp: 893 – 895, 2009.
26. B. Kovalerchuk, Fuzzy logic in computer-aided breast cancer diagnosis: analysis of lobulation, Artificial Intelligence in Medicine, Volume 11, Issue 1, September 1997, pp: 75-85, 1997.
27. J. R. Perez-Gallardo, B. Hernandez-Vera, A. A. Aguilar-Lasserre, R. Posada-Gomez, Interpretation of Mammographic Using Fuzzy Logic for Early Diagnosis of Breast Cancer, Proceedings of the 2008 Seventh Mexican International Conference on Artificial Intelligence, pp: 278-283, 2008.
28. R. Jain, A. Abraham, A Comparative Study of Fuzzy Classification Methods on Breast Cancer Data, 7th International Work Conference on Artificial and Natural Neural Networks, IWANN’03, Spain, 2003
29. H.D. Cheng, A neural network for breast cancer detection using fuzzy entropy approach, 1995 International Conference on Image Processing (ICIP'95) - Volume 3, 1995.
30. S. He, Q. H. Wu, J. R. Saunders, Breast cancer diagnosis using an artificial neural network trained by group search optimizer, Transactions of the Institute of Measurement and Control, Vol. 31, No. 6. (1 December 2009), pp: 517-531, 2009.
31. S. Hwang, D. Kim, T. Kang and G. Park, Medical Diagnosis System of Breast Cancer Using FCM Based Parallel Neural Networks, Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, Lecture Notes in Computer Science, 2007, Volume 4682/2007, 712-719, 2007.
32. L. K. Hansen and P. Salamon, Neural Network Ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. 12, No. 10, pp. 993-1001, 1990
33. J. Sharkey, On combining artificial neural networks. Connection Science, 8(3& 4):299–313, 1996.
34. L. Xu, A. Krzyzak, and C. Y. Suen, Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics 22:418–435, 1992.
35. N. J. Nilsson, Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw Hill, New York, 1965.
36. R. Clemen, Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, pp: 559– 583, 1989.
37. J. A. Barnett, Computational methods for a mathematical theory of evidence. In proceedings of IFCAI, pp: 868–875, 1981.
38. C. W. J. Granger, Combining forecasts-twenty years later, International Journal of Forecasting, pp: 8, 1989.
39. S. Haykins, Neural Networks, A comprehensive Foundation. Macmillan College Publishing Company, 1994.
40. R. Jacobs, M. Jordan and G. Hinton, Adaptive mixture of local experts, Neural Computation, 3(1):79–87, 1991.
41. C. Bauckhage and C. Thurau, Towards a fair’n square aimbot—Using mixture of experts to learn context aware weapon handling, in: Proccedings of GAME–ON, Ghent, Belgium, 2004, pp. 20–24.
42. P. Hartono and S. Hashimoto, Ensemble of linear perceptrons with confidence level output, Proceedings of 4th Intl. Conf. on Hybrid Intelligent Systems, 2004, pp. 186–191, 2004.
43. Verikas, A. Lipnickas, K. Malmqvist, M. Bacauskiene and A. Gelzinis, Pattern Recognition Letters 20 (1999), pp: 429—444, 1999.
44. R. A. Jacobs. Methods of combining experts’ probability assessments. Neural Computation, 7: 867–888, 1995.
45. O. Castillo and P. Melin, Soft Computing for Control of Non-Linear Dynamical Systems. Springer, Heidelberg (2001).
46. B. Verma, P. Mcleod and A. Klevansky, Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer, Expert Systems with Applications, Elsevier,Volume 37, Issue 4, pp: 3344-3351, 2010.
47. S. J. Kuo, Y. H. Hsiao, Y. L. Huang and D. R. Chen, Classification of benign and malignant breast tumors using neural networks and three-dimensional power Doppler ultrasound, Ultrasound in Obstetrics and Gynecology, Volume 32, Issue 1, pp: 97–102, 2008.
48. M. Karabatak and M. Cevdet Ince, An expert system for detection of breast cancer based on association rules and neural network, Expert Systems with Applications, Elsevier,Volume 36, Issue 2, Part 2, pp: 3465-3469, 2009.
49. C. M. M. Pareira and R. F. De Mello, A Radial Basis Function Neural Network approach to detect novelties: Applications on health datasets, Proceedings of the Joint Conferences on Pervasive Computing (JCPC), pp: 143 – 148, 2009.
50. V. Del Mar-Valparaiso, Breast cancer diagnosis based on a suitable combination of deformable models and artificial neural networks techniques, Proceedings of the Congress on pattern recognition 12th Ibero-american conference on Progress in pattern recognition, image analysis and applications, Lecture Notes In Computer Science, pp: 803-811, 2007.
51. R. R. Janghel, A. Shukla, R. Tiwari and R. Kala, Intelligent Decision Support System for Breast Cancer, Advances in Swarm Intelligence, Lecture Notes in Computer Science, Volume 6146/2010, 2010.
52. E. d. Ubeyli, Comparison of different classification algorithms in clinical decision-making, Expert Systems, Wiley, Volume 24, Issue 1, pp: 17–31, 2007.
53. E. d. Ubeyli, Implementing automated diagnostic systems for breast cancer detection, Expert Systems with Applications, Elsevier, Volume 33, Issue 4, pp: 1054-1062, 2007.
54. T. Kuyan and T. Yildirim, Breast cancer diagnosis using statistical neural networks, Journal of Electrical & Electronics Engineering, Volume: 4, Number: 2, 2004.
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2017-05-06
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Janghel, R. R., & Shukla, A. (2017). Expert system for breast cancer diagnosis using ensemble approach. CSVTU International Journal of Biotechnology, Bioinformatics and Biomedical, 1(1), 1–7. https://doi.org/10.30732/ijbbb.20160101001
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