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Reseach Article

A Survey on Various Classification Techniques for Medical Image Data

by Niranjan J. Chatap, Ashish Kr. Shrivastava
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 15
Year of Publication: 2014
Authors: Niranjan J. Chatap, Ashish Kr. Shrivastava
10.5120/17080-7528

Niranjan J. Chatap, Ashish Kr. Shrivastava . A Survey on Various Classification Techniques for Medical Image Data. International Journal of Computer Applications. 97, 15 ( July 2014), 1-5. DOI=10.5120/17080-7528

@article{ 10.5120/17080-7528,
author = { Niranjan J. Chatap, Ashish Kr. Shrivastava },
title = { A Survey on Various Classification Techniques for Medical Image Data },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 15 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume97/number15/17080-7528/ },
doi = { 10.5120/17080-7528 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:24:09.521304+05:30
%A Niranjan J. Chatap
%A Ashish Kr. Shrivastava
%T A Survey on Various Classification Techniques for Medical Image Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 15
%P 1-5
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a survey on various classification techniques for medical image and also its application for detection of many diseases. Several classification techniques are investigated till today. One of the best methods for classification techniques artificial neural network and SVM (Support Vector Machine). In past many classification techniques by using GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) are commonly used. The classification techniques provide invaluable information to pathologist for diagnosis and treatment of diseases. By identifying and counting blood cell within the blood smear using classification techniques it's quite possible to detect so many diseases. If we use one of the new classifier i. e. nearest neighbor and SVM it is quiet possible to detect the cancer cell from the blood cell counting.

References
  1. S. N. Deepa and B. Aruna Devi, "A survey on artificial intelligence approaches for medical image classification"; IJST, vol 4, No. 11 (Nov 2011).
  2. Breiman;L; Fredman, J. H. , Olshen, R. A. , & Stone C J. (1984). Classification and Regression Trees. Wodsworth, Belmont.
  3. Quinlan J. R. (1994) comparing Connectionist and Symbolic Learning Methods". Computational Learning Theory and National Learning Systems: Constraints and prospects,1.
  4. Quinlm R (1998) C5. 0: An informal Tutorial. Rule quest. Retrieved from http://www. rulequest. com/sec5-win. html
  5. Kass G. V. (1980). Applied statistics, 29, 119-127
  6. Lim, T, Loh W, &Shih . y (2000). A comparison of predictive accuracy complexity and Training time of Thirty. Three old and new classification algorithms Machine Learning, 40(3), 203-228, Klywer academic publication, Boston.
  7. Klecka, W. R. (1980) "Discriminant Analysis Quantitative Application in the social Sciences Series", No. 19. Thousand Oaks, CA: sage publication.
  8. Larose D. T. (Ed. ) (2005). Discovering knowledge in Data: An introduction to Data mining. Hoboken, NJ : Wiley.
  9. Larose, D. T. (Ed. ) (2006) Data mining Methods and Models. Hoboken, NJ: Wiley.
  10. McCallum A,& Nigam K. (1998). A comparison of Event Models for Naïve Bayes Text classification. Proceedings of workshop on Learning for Text categorization. American Association for Artificial Intelligence.
  11. Smitha P. Shaji L, Dr. Mini M. G. , " A Review of Medical Image classification Techniques", International conference on VLSI communication R Instrumentation (ICVCI) 2011 (IJCA).
  12. Hota H. S. Shukla S. P. and GulhareKajalKiran, "Review of Intelligent Techniques Applied for classification and preprocessing of Medical Image Data", IJCSI, Vol. 10, Issue 1, No. 3, January 2013, ISSN : 1694-0784, ISSNLONLONE : 1694-0814
  13. XIAOSHENG WANG OSAMU GOTOH, "Cancer classification using single Genes". Microarray- Based cancer Perdition using soft computing Approach, cancer information, 7: 123-139, 2009.
  14. N. Revathy, Dr. R. Amalraj, "Accurate Cancer Classification using Expressions of very Few Genes", International Journal of computer Application (0975-8887), Vol. 14, No. -4, January 2011.
  15. D. Jegelevicius, A. Krisciukaitis, A. Lukosevicius, V. Marozas, A. Paunksnis, V. Barzdziukas, et al, "Network Based Clinical Decision Support System" Proceedings of the 9th International Conference on Info~ation Technology and Applications in Biomedicine, ITAB 2009, Larnaca, Cyprus, November 2009
  16. Wei-Liang Tai, Rouh-Mei Hu, Han C. W. Hsiao, Rong-Ming Chen, and Jeffrey J. P. Tsai, "Blood Cell Image Classification Based on Hierarchical SVM", 2011, IEEE, International Symposium on Multimedia 978-0-7695-4589-9111, 2011 IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Medical imaging classification technique Artificial Neural Network Nearest Neighbor Network SVM