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Novel Fuzzy Association Rule Image Mining Algorithm for Medical Decision Support System

by P. Rajendran, M. Madheswaran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 20
Year of Publication: 2010
Authors: P. Rajendran, M. Madheswaran
10.5120/415-613

P. Rajendran, M. Madheswaran . Novel Fuzzy Association Rule Image Mining Algorithm for Medical Decision Support System. International Journal of Computer Applications. 1, 20 ( February 2010), 87-94. DOI=10.5120/415-613

@article{ 10.5120/415-613,
author = { P. Rajendran, M. Madheswaran },
title = { Novel Fuzzy Association Rule Image Mining Algorithm for Medical Decision Support System },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 20 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 87-94 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number20/415-613/ },
doi = { 10.5120/415-613 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:47:23.701569+05:30
%A P. Rajendran
%A M. Madheswaran
%T Novel Fuzzy Association Rule Image Mining Algorithm for Medical Decision Support System
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 20
%P 87-94
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The proposed method deals with the detection of brain tumor in the CT scan brain images. The preprocessing technique applied on the images eliminates the inconsistent data from the CT scan brain images. Then feature extraction process is applied to extract the features from the brain images. A Novel Fuzzy Association Rule Mining (NFARM) applied on the image transaction database which contains the features that are extracted from the CT scan brain images. A new test image has been tested with the mined (NFARM) rules. The proposed NFARM gives the diagnosis keywords to physicians for making a better diagnosis system. The experimental results of the proposed method gives better performance compared to the traditional Fuzzy Apriori algorithm.

References
  1. Serhat Ozekes.,A.Yilmez Camurc :Computer aided detection of Mammographic masses on CAD digital Mammograms.: stanbul Ticaret Üniversitesi Fen Bilimleri (2005) pp.87-97
  2. Ruchaneewan Susomboon, Daniela Stan Raicu, Jacob Furst.:Pixel – Based Texture Classification of Tissues in computed Tomography.: Literature review (2007)
  3. De Cock, M.Cornelos, C.Kerre E.E. : Fuzzy Association Rules : A Two – sided Approach In : FIP, PP 385-390(2003)
  4. Yan, p, Chen, G, Corneils, C,De Cock, M, Kerre, E.E: Mining Positive and Negative Fuzzy Association Rules In : KES, PP 270-276 Springes (2004)
  5. Verlincle, H, De Cock, M, Boute, R: Fuzzy Versus Quantitative Association Rules : A Fair Data- Driven Comparision: IEEE Transactions on Systems, Man, and Cybernetics- Part B : Cybernetics, 36,679-683 ( 2006)
  6. Agrwal. R., Imielinsk., T, Swam., A.N. : Mining Association Rules between sets of Items in Large Databases.:. S/G Mod Record 22, 207-216(1993)
  7. Laila Elfangary and Walid Adly Atteya: Mining MedicalDatabases using Proposed Incremental Association Rules Algorithm (PIA).: Second International Conference on the Digital Society ,IEEE Computer Society(2008)
  8. Pudi. V., Harilsa., j. : On the optimality of association rule mining algorithms. Technical Report TR-2001-01, DSL, Indian Institute of Science (2001)
  9. Hoppner. F., Klawonn. F.; Kruse, R, Rurkler, T.: Fuzzy cluster Analysis, methods for classification Data Analysis and Image recognition.: Wiley, New York (1999)
  10. Delgado. M., Marin. N., Sanchez. D., Vila. M.A. : Fuzzy Association Rules : General Model and Applications. IEEE Transaction of Fuzzy systems 11, 214-225(2003)
  11. Shu-yue. J., Tsung. E., Yenng. D., Daming. S.: Mining Fuzzy association rules with weighted items In: IEEE International Conference on SMC, pp 1906-1911, IEEE(2000)
  12. Cheng., Yan. P., Kerre. E.E.: Computationally Efficeint mining for Fuzzy Implication Based Association Rules in Quantitative Database.: International Journal of General systems, 33, 163-182(2004)
  13. J.S. Park, M. Chen, and P.S. Yu, “An Effective Hash Based Algorithm for Mining Association Rules,” Proc. ACM IGMOD,(1995).
  14. Zaiane. O.R. Har .J.: Multimedia Miner: a system prototype for multimedia data mining.: , in : Proc. ACM- SIG MOD, Seattle, 1998, pp 581-583
  15. Wirth. M.D., Nikitenko.J. : Segmentation of the Breast Region in Mammograms using a Rule – Based Fuzzy Reasoning Algorithm.: ICGST-GVIP Journal, Volume 3 Issue on, Jan 2005
  16. C.Oronez, E. Omiecinski.; Discovering association rules based on image content, in: IEEE Advances in Digital Libraries Conference, 1999
  17. Wynne Hsu, MongLiLee, Ji Zhang, : Image Mining : trends and developments, Journal of Intelligent Information systems 9(1) (2002) 7-23.
  18. Maria-Luiza Antonie, Osmar R. Zaiane, Alexandra Coman, : Application of data mining techniques for medical image classification. In : Proc. Second Int. WorkShop on Multimedia Data Mining (MDM/KDD’ 2001)
  19. Tiawei Han and Micheline Kamber.: Data Mining Concepts & techniques.: Morgan Kaufmann,2001
  20. Fafael C.Gonzalez and Richard E. Woods.: Digital Image Processing 2nd edition. Addision Wesley 1993
  21. Hanchuan Peng, Fubui Long, and Chris Ding.: Feature Selection based on mutual information: Criteria of Max- dependency,Max_relerance and Min_redundancy.:IEEE Transaction on Pattern Analysis and machine Intelligence, Vol. 27 , No. 8, pp . 1226-1238,2005
Index Terms

Computer Science
Information Sciences

Keywords

Novel Fuzzy Association Rule Mining (NFARM) classification Pre processing Feature Extraction medical imaging image mining data mining image mining data mining