Knowledge Based Approach for Diagnosis of Breast Cancer

2009 IEEE International Advance Computing Conference, IACC 2009

Volumn , Issue , 2009, Pages 6-12

  Shukla, Anupam ^a   Tiwari, Ritu ^a   Kaur, Prabhdeep ^a  

^a ABV INDIAN INSTITUTE OF INFORMATION TECHNOLOGY AND MANAGEMENT   (India)

Author keywords

Back propagation algorithm; Breast cancer; Comparison; Diagnosis; Knowledge based system; Learning vector quantization networks; Neural networks; Neuro fuzzy systems; Radial basis function networks

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANN ALGORITHM; ARTIFICIAL NEURAL NETWORKS; BREAST CANCER; COMPARISON; EARLY DETECTION OF BREAST CANCER; KNOWLEDGE-BASED APPROACH; LEARNING VECTOR QUANTIZATION NETWORKS; NEURO-FUZZY SYSTEMS; NEUROFUZZY SYSTEM; SIMULATION RESULT; SOFT COMPUTING TOOLS; SURVIVAL RATE; TRAINING TIME;

ARTIFICIAL INTELLIGENCE; ATTITUDE CONTROL; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; EDUCATION; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; MATLAB; SOFT COMPUTING; VECTOR QUANTIZATION;

RADIAL BASIS FUNCTION NETWORKS;

EID: 66249120732     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IADCC.2009.4808971     Document Type: Conference Paper

References (32)

1. Rupert Bardens, Dimitris Karagiannis, Knowledge based systems: an approach for manpower planning", Angewandte Informatik, Volume 30, Issue 2 (February 1988), Pages: 71 -80. (Pubitemid 18603887)
2. I. S. Torsun, Book on Foundations of Intelligent Knowledge-Based Systems", Academic Press; 1 edition (August 28, 1995), AMAZON.COM.
3. Antal, P., Meszaros, T., De Moor, B. and Dobrowiecki, T. 2001, in Proceedings of Fourteenth IEEE Symposium on Computer-Based Medical Systems (CBMS 2001), July 26-27, Bethesda, MD, 177-182.
4. Dybowski, R., and Gant V.:'Clinical Applications of Artificial Neural Networks' (Cambridge University Press, 2001).
5. Lisboa P. J. G., Vellido, A., and Wong, H.: 'Outstanding Issues for Clinical Decision Support with Neural Networks', Artificial Neural Networks in Medicine and Biology, pp. 63-7 1, Springer, 2000.
6. Breast Cancer: Statistics on Incidence, Survival, and Screening from http://imaginis.com/breasthealth/statistics.asp, accessed January 2008.
7. Cancer Research UK. 2004, Summary of Specific Cancers. http://www.cancerresearchuk.org accessed December 2007.
8. http://en.wikipedia.orgwiki/Breast-cancer accessed February 2008.
9. J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro- fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Englewood Cliffs, NJ: Prentice-Hall, 1996.
10. Floyd, C.E., Lo, J.Y., Yun, A.J., Sullivan, D.C., and Kornguth, P.J.: 'Prediction of breast cancer malignancy using an artificial neural network'. Cancer, 74:2944-2998, 1994. (Pubitemid 24361079)
11. Furundzic, D., Djordjevic, M., and Bekic A.J.: 'Neural networks approach to early breast cancer detection'. Systems Architecture, 44:617-633, 1998. (Pubitemid 128406627)
12. Meesad P., and Yen, G. G.: 'Combined Numerical and Linguistic Knowledge Representation and Its Application to Medical Diagnosis', IEEE Transactions on Systems, Man, and Cybernatics, 2003.
13. Pena-Reyes, C. A., and Sipper M.: 'Designing Breast Cancer Diagnostic System via a Hybrid Fuzzy- Genetic Methodology'. IEEE International Fuzzy Systems Conference Proceedings, 1999.
14. Setiono, R.: 'Extracting rules from pruned neural networks for breast cancer diagnosis', Artificial Intelligence in Medicine, 8:37-5 1, 1996. (Pubitemid 26077270)
15. Setiono, R.: 'Generating concise and accurate classification rules for breast cancer diagnosis',Artificial Intelligence in Medicine, 18:205-219, 2000. (Pubitemid 30089529)
16. Setiono, R., and Huan L.: 'Understanding neural networks via rule extraction'. Proceedings of the International Joint Conference on Artificial Intelligence, pages 480-487. Morgan Kaufmann, 1995
17. Setiono, R., and Huan L.: 'Neurolinear: from neural networks to oblique decision rules'. Neurocomputing, 17:1-24, 1997. (Pubitemid 27433442)
18. Song, H. J., Lee, S. G., and Park, G.T.: 'A Methodology of Computer Aided Diagnostic System on Breast Cancer'. Proceedings of the 2005 IEEE Conference in control Applications, Toronto, Canada, Aug 28-31, 2005, pp. 831- 836. (Pubitemid 43896076)
19. Walker, H. L. Jr., and Elizabeth, A. V.: 'Experiments Using an Evolutionary Programmed Neural Network with Adaptive Boosting for Computer Aided Diagnosis of Breast Cancer'. IEEE International Workshop on Soft Computing in Industrial Application, 2003.
20. Wang, J., and Lee, C. S. G.: 'Self-Adaptive Neuro-Fuzzy Inference Systems for Classification Applications', IEEE Trans. on Fuzzy Systems, 2002.
21. Basheer, I.A., and Hajmeer, M.: 'Artificial neural networks: fundamentals, computing, design, and application', J. Microbiol. Methods 43 (2000) 3-31.
22. Haykin, S.: 'Neural networks - a comprehensive foundation' (Prentice Hall, New Jersey, USA, 2 edition, 1999).
23. Freeman, J. A., and Skapura, D. M.: 'Neural Networks'( Addison-Wesley, 1999).
24. Orr, M.J.L.: 'Regularisation in the Selection of Radial Basis Function Centres', Neural Computation, 7(3): 606-623, 1995.
25. Toh, K.A.[Kar-Ann], Mao, K.Z., A global transformation approach to RBF neural network learning, ICPR02(II: 96-99).
26. N. Jankowski, Approximation with RBF-type Neural Networks using flexible local and semi-local transfer functions, 4th Conference on Neural Networks and Their Applications, pp. 77-82, May 1999
27. Ghosh, A., Biehl, M., Freking, A. and Reents, G.: 2004, A theoretical framework for analyzing the dynamics of LVQ: A statistical physics approach, Technical Report 2004-9-02, Mathematics and Computing Science, University Groningen, P.O. Box 800, 9700 AV Groningen, Netherlands, available from www.cs.rug.nl/biehl.
28. Hammer, B. and Villmann, T.: 2002, Generalized relevance learning vector quantization, Neural Networks 15(8-9), 1059-1068.
29. Neural Networks Research Centre, Helsinki: 2002, Bibliography on the self-organizing maps (SOM) and learning vector quantization (LVQ), Otaniemi: Helsinki Univ. of Technology. Available on-line:http://www.ira.ukade/ bibliograph/neural/SOM.L//VQhtml.
30. Abraham, A. 2001, Neuro-Fuzzy Systems: State- of-the-Art Modeling Techniques. In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Springer, Berlin, J. Mira,A. Prieto (Eds.). (Pubitemid 33291829)
31. Jeen-Shing Wang and C. S. George Lee, Self- Adaptive Neuro-Fuzzy Inference Systems for Classification Applications", IEEE Transactions on Fuzzy Systems, 2002.
32. UCI repository of machine learning databases, http://archive.ics.uci.edu/ ml/datasets.html accessed October 2007.