CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Breast Cancer Diagnosis System using Hybrid Case-based Approach

by Dina A. Sharaf-el Deen, Ibrahim F. Moawad, M. E. Khalifa
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 23
Year of Publication: 2013
Authors: Dina A. Sharaf-el Deen, Ibrahim F. Moawad, M. E. Khalifa
10.5120/12681-9450

Dina A. Sharaf-el Deen, Ibrahim F. Moawad, M. E. Khalifa . A Breast Cancer Diagnosis System using Hybrid Case-based Approach. International Journal of Computer Applications. 72, 23 ( June 2013), 14-20. DOI=10.5120/12681-9450

@article{ 10.5120/12681-9450,
author = { Dina A. Sharaf-el Deen, Ibrahim F. Moawad, M. E. Khalifa },
title = { A Breast Cancer Diagnosis System using Hybrid Case-based Approach },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 23 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number23/12681-9450/ },
doi = { 10.5120/12681-9450 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:38:51.019766+05:30
%A Dina A. Sharaf-el Deen
%A Ibrahim F. Moawad
%A M. E. Khalifa
%T A Breast Cancer Diagnosis System using Hybrid Case-based Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 23
%P 14-20
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays, mammography is recognized as the most effective technique for breast cancer diagnosis. Case-Based Reasoning (CBR) is one of the important techniques used to diagnose the breast cancer disease. The retrieval-only CBR systems do not provide an acceptable accuracy in critical domains such as medical. In this paper, a new breast cancer diagnosis system using hybrid case-based approach is presented to improve the accuracy of the retrieval-only CBR systems. The approach integrates case-based reasoning and rule-based reasoning, and applies the adaptation process automatically by exploiting adaptation rules. Both adaptation rules and reasoning rules are generated automatically from the case-base. After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated automatically. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose the breast cancerdisease. The final results showed that the proposed approach increases the diagnosing accuracy comparing with the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer diagnosis systems.

References
  1. Ahmed, M. U. , Begum, S. , & Funk, P. 2012. A Hybrid Case-Based System in Stress Diagnosis and Treatment. In IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI2012).
  2. Sahin, S. , Tolun, M. R. , &Hassanpour, R. 2012. Hybrid expert systems: A survey of current approaches and applications. Expert Systems with Applications, 39(4), 4609-4617.
  3. Bichindaritz, I. , &Montani, S. 2011. Guest Editorial: Advances in case-based reasoning in the health sciences. Artificial Intelligence in Medicine, 51(2), 75-79.
  4. Kolodner, J. 1993. Case based reasoning. Morgan Kauffman, San Mateo, California
  5. Aamodt, A. , & Plaza, E. 1994. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI communications, 7(1), 39-59.
  6. Baumeister, J. , Atzmuller, M. , Puppe, F. 2002. Inductive learning for case-based diagnosis with multiple fault, in: Advanced in Case-Based Reasoning: 6thEuropean Conference, ECCBR, Springer, Berlin, pp. 28-42
  7. Hunt, J. , Miles, R. 1994. Hybrid case-based reasoning, The Knowledge Engineering Review,vol. 9(4), pp. 383-397.
  8. Lenz, M. , Bartsch-Spörl, B. , Burkhard, H. D. , &Wess, S. 1998. Case-based reasoning technology, from foundations to applications. Springer-Verlag.
  9. Macura, R. T. , &Macura, K. 1997. Case-based reasoning: opportunities and applications in health care. Artificial Intelligence in Medicine, 9(1), 1.
  10. Begum, S. , Ahmed, M. U. , Funk, P. , Xiong, N. , &Folke, M. 2011. Case-based reasoning systems in the health sciences: a survey of recent trends and developments. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 41(4), 421-434.
  11. Sharaf-Eldeen, D. A. , Moawad, I. F. , El Bahnasy, K. , &Khalifa, M. E. 2012. Learning and Applying Range Adaptation Rules in Case-Based Reasoning Systems. In Advanced Machine Learning Technologies and Applications (pp. 487-495). Springer Berlin Heidelberg.
  12. Blanco, X. , Rodríguez, S. , Corchado, J. M. , &Zato, C. 2013. Case-Based Reasoning Applied to Medical Diagnosis and Treatment. In Distributed Computing and Artificial Intelligence (pp. 137-146). Springer International Publishing.
  13. Li, H. , Li, X. , Hu, D. , Hao, T. , Wenyin, L. , & Chen, X. 2009. Adaptation rule learning for case?based reasoning. Concurrency and Computation: Practice and Experience, 21(5), 673-689.
  14. Yusof, M. M. , & Buckingham, C. D. (2009). Medical case-based reasoning: A review of retrieving, matching and adaptation processes in recent systems. InProceedings of the IASTED International Conference (Vol. 639, p. 053).
  15. Huang, M. J. , Chen, M. Y. , & Lee, S. C. 2007. Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Systems with Applications, 32(3), 856-867.
  16. Schmidt, R. , &Vorobieva, O. 2006. Case-based reasoning investigation of therapy inefficacy. Knowledge-Based Systems, 19(5), 333-340.
  17. Marling, C. , Shubrook, J. , & Schwartz, F. 2008. Case-based decision support for patients with type 1 diabetes on insulin pump therapy. In Advances in Case-Based Reasoning (pp. 325-339). Springer Berlin Heidelberg.
  18. Schmidt, R. , &Gierl, L. 2005. A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning. International journal of medical informatics, 74(2), 307-315.
  19. Ochoa, A. , Meza, M. , González, S. , Padilla, A. , Damiè, M. , Torre, D. L. J. , & Jiménez-Vielma, F. 2008. An intelligent tutor based on case-based reasoning to medical use. Advances in Computer Science and Engineering. Research in Computing Science. , 34, 187-194.
  20. O'Sullivan, D. , Bertolotto, M. , Wilson, D. , &McLoghlin, E. 2006. Fusing mobile case-based decision support with intelligent patient knowledge management. In Workshop on CBR in the Health Sciences (pp. 151-160).
  21. Ayer, T. , Ayvaci, M. U. , Liu, Z. X. ,Alagoz, O. , & Burnside, E. S. 2010. Computer-aided diagnostic models in breast cancer screening. Imaging, 2(3), 313-323.
  22. Huang, M. L. , Hung, Y. H. , Lee, W. M. , Li, R. K. , & Wang, T. H. 2012. Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis. Journal of medical systems, 36(2), 407-414.
  23. Pawlak, Z. 1982. Rough sets. International Journal of Computer and Information Sciences, 11(5), 341 – 356.
  24. Yang, Y. P. O. , Shieh, H. M. , Tzeng, G. H. , Yen, L. , & Chan, C. C. 2008. Business aviation decision-making using rough sets. In Rough Sets and Current Trends in Computing (pp. 329-338). Springer Berlin Heidelberg.
  25. Shyng, J. Y. , Wang, F. K. , Tzeng, G. H. , & Wu, K. S. 2007. Rough set theory in analyzing the attributes of combination values for the insurance market. Expert Systems with Applications, 32(1), 56-64.
  26. Ziarko, W. P. , & Van Rijsbergen, C. J. 1994. Rough sets, fuzzy sets and knowledge discovery (Vol. 15). London: Springer-Verlag.
  27. "http://archive. ics. uci. edu/ml/datasets. html", last accessed: May 2013
  28. Salzberg, S. L. 1997. On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and knowledge discovery, 1(3), 317-328.
  29. Jiang, Y. , et al. 1996. Malignant and benign clustered microcalcifications: automated feature analysis and classification. Radiology,198(3), 671-678.
  30. Markopoulos, C. , Kouskos, E. , Koufopoulos, K. , Kyriakou, V. , &Gogas, J. 2001. Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography. European journal of radiology, 39(1), 60-65.
  31. Huo, Z. , Giger, M. L. ,Vyborny, C. J. , & Metz, C. E. 2002. Breast Cancer: Effectiveness of Computer-aided Diagnosis—Observer Study with Independent Database of Mammograms1. Radiology, 224(2), 560-568.
  32. Floyd, C. E. , Lo, J. Y. , &Tourassi, G. D. 2000. Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions. American Journal of Roentgenology, 175(5), 1347-1352.
  33. Elter, M. , Schulz-Wendtland, R. , & Wittenberg, T. 2007. The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Medical Physics, 34, 4164.
  34. Chan, H. P. , et al. 1999. Improvement of Radiologists' Characterization of Mammographic Masses by Using Computer-aided Diagnosis: An ROC Study1. Radiology, 212(3), 817-827.
  35. Gupta, S. , Chyn, P. F. , & Markey, M. K. 2006. Breast cancer CAD based on BI-RADS™ descriptors from two mammographic views. Medical physics, 33, 1810.
  36. Wang, X. H. , Zheng, B. , Good, W. F. , King, J. L. , & Chang, Y. H. 1999. Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network. International Journal of Medical Informatics, 54(2), 115-126.
  37. Chhatwal, J. , Alagoz, O. , Lindstrom, M. J. , Kahn, C. E. , Shaffer, K. A. , & Burnside, E. S. 2009. A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. American Journal of Roentgenology, 192(4), 1117-1127.
  38. Burnside, E. S. , et al. 2009. Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings1. Radiology, 251(3), 663-672.
  39. Ayer, T. , Alagoz, O. , Chhatwal, J. , Shavlik, J. W. , Kahn, C. E. , & Burnside, E. S. 2010. An artificial neural network to quantify malignancy risk based on mammography findings: discrimination and calibration. Cancer.
  40. Bilska-Wolak, A. O. , Floyd Jr, C. E. , Lo, J. Y. , & Baker, J. A. 2005. Computer aid for decision to biopsy breast masses on mammography: validation on new cases. Academic radiology, 12(6), 671.
  41. Park, S. H. , Goo, J. M. , & Jo, C. H. 2004. Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean Journal of Radiology, 5(1), 11-18.
Index Terms

Computer Science
Information Sciences

Keywords

Case-based reasoning (CBR) Rule-based reasoning (RBR) Adaptation rules Breast cancer diagnosis Mammography

Powered by PhDFocusTM