CFP last date
20 June 2024
Reseach Article

Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review

by Parveen Khan, Amritpal Singh, Saurabh Maheshwari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 21
Year of Publication: 2014
Authors: Parveen Khan, Amritpal Singh, Saurabh Maheshwari

Parveen Khan, Amritpal Singh, Saurabh Maheshwari . Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review. International Journal of Computer Applications. 98, 21 ( July 2014), 13-19. DOI=10.5120/17306-7741

@article{ 10.5120/17306-7741,
author = { Parveen Khan, Amritpal Singh, Saurabh Maheshwari },
title = { Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 21 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 13-19 },
numpages = {9},
url = { },
doi = { 10.5120/17306-7741 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:26:46.781130+05:30
%A Parveen Khan
%A Amritpal Singh
%A Saurabh Maheshwari
%T Automated Brain Tumor Detection in Medical Brain Images and Clinical Parameters using Data Mining Techniques: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 21
%P 13-19
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Data mining is a growing field of research that intersects with many other fields such as Artificial Intelligent, Statistics, Visualization, Parallel Computing and Image Processing. Data mining techniques are good for Brain MRI image classification that can diagnose brain tumor and other diseases. In this paper we present an overview of the current research being carried out using the data mining techniques for the diagnosis of brain tumor. The goal of this study is to identify the most well performing data mining algorithms used on medical brain MRI and Clinical parameters. The following algorithms have been identified: Decision Trees, Support Vector Machine, Artificial Neural Networks and their Multilayer Perceptron model, and Fuzzy C-Means. Analyses show that it is very difficult to name a single data mining algorithm as the most suitable for the brain tumor detection or classification. At times some algorithms perform better than others, but there are cases when the properties of some of the above mentioned algorithm are combined together, they provide effective result. This paper also provides a critical evaluation of the literature reviewed, which reveals new facets of.

  1. Freitas, A. A and Lavington, S. H. 1998. Mining Very Large Databases with Parallel Processing. Kulwer Academic Publishers,. ISBN 0-7923-8048-7.
  2. Bayardo R. J. 1998. Efficiently mining long patterns from databases. In ACM SIGMOD Conf. Management of Data.
  3. Joshi, D. M. , Rana, N. K. and Mishra, V. M. 2010. Classification of Brain Cancer Using Artificial Neural Network. International Conference on Electronic Computer Technology, pp-112-116.
  4. CancerNet, A service of the National Cancer Institute. [Online]. Available: http://www. cancer. gov/cancertopics/types/brain tumor
  5. Arizmendi, C. , Tamames, J. H. , Romero, E. , Vellido, A. and Pozo, F. D. 2010. Diagnosis of Brain Tumors from Magnetic Resonance Spectroscopy using Wavelets and Neural Networks. Annual International Conference of the IEEE EMBS (2010), pp-6074-6077.
  6. Gupta, D. S. , Sood, M. , Vijayvargia, P. S. and Hota, P. K. 2013. Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor, ncbi. nlm. nih. gov/pmc/articles/PMC3717182/.
  7. Bandyopadhyay, S. K. and Paul, T. U. 2013. "Automatic Segmentation of Brain Tumour from Multiple Images of Brain MRI", International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 2, Issue 1, January.
  8. Shi, Z. , He, L. , Suzuki, T. N. K. and Itoh, H. 2009. "Survey on Neural Networks used for Medical Image Processing", International Journal of Computational Science.
  9. Hsu, C. W. , Chang, C. C. and Lin C. J. 2010. Practical Guide to Support Vector Classification. http://www. csie. ntu. edu. tw/~cjlin.
  10. Shanmugapriya, K. P. , Srinivasan, B. and Narendran, P. 2013 "A study on applications of data mining techniques in brain imaging", International Journal of Advanced Research in Data Mining and Cloud Computing Vol. 1, Issue 1.
  11. Jesmin, T. , Ahmed, K. , Zamilur and M. , Alam, M. B. 2013. "Brain Cancer Risk Prediction Tool Using Data Mining", International journal of Computer Application,volume 61-No. 12.
  12. Kumar, L. S. and Padmapriya, A. 2012. "ID3 Algorithm Performance of Diagnosis For Common Disease", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 5.
  13. Ibrahim, A W. H. , . Osman, A. A. and Mohamed, Y. I. 2013. MRI brain image classification using neural networks. International conference on computing, electrical and electronics engineering (ICCEEE).
  14. Amato, F. , Lopez, A. , Mendez, E. M. P. , Vanhara, P. , Hampl, A. and Havel, J. 2013. "Artificial neural networks in medical diagnosis", J Appl Biomed. 11: 47–58.
  15. Shah, S. and Parikh, S. 2010. Issues in medical diagnosis using Computational techniques. fourth international conference on computational intelligence and communication networks, IEEE.
  16. Rajendran, P. , Madheswaran, M. and Naganandhini, K. 2010. An Improved Pre-Processing Technique with Image Mining Approach for the Medical Image Classification. Second International conference on Computing, Communication and Networking Technologies,
  17. Isola, R. and Carvalho, R. 2012. "Knowledge discovery in medical systems using Differential diagnosis, LAMSTAR, and k-NN", IEEE transactions on information technology in biomedicine, vol. 16, no. 6.
  18. Padma, A. and Sukanesh, R. 2013. "SVM based classification of soft tissues in brain CT images using wavelet based dominant gray level run length texture features", middle-east journal of scientific research 13(7): 883-888.
  19. Abdullah, N. , Ngah, U. K. , and Aziz, S. A. 2011. "Image Classification of Brain MRI using support vector machine", IEEE.
  20. Ubaidillah, S. H. S. A. , Sallehuddin, R. and Ali, N. A. 2013. "Cancer detection using artificial neural network and support vector machine:A Comparative study", jurnal teknologi(science & engineering) 65:1.
  21. Rajendran, P. and Madheswaran, M. 2010. "Hybrid medical image classification using association rule mining with decision tree algorithm", journal of computing, volume 2, issue 1.
  22. Subhashini, M. M. and Shood, S. K. 2012. Brain tumor detection using pulse couple neural network (PCNN) and back propagation network. Third International Conference on Sustainable Energy and Intelligent System, VCTW, Tiruchengode, Tamilnadu, India on 27-29.
  23. Gupta, V. and Sagale, K. S. 2012. Implementation a classification system. Nirma University International Conference On Engineering, 06 08
  24. Badarnel, A. A. , Nafadat, H. and Alraziqi A. M. 2012. A classifier to detect tumor disease in brain MRI brain images. ACM international conference on advances in social networks analysis and mining, IEEE.
  25. S. N. Deepa and B. A. Devi 2012. Artificial neural networks design for classification of brain tumour. International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, Coimbatore, INDIA.
  26. Kovacevic, D. and Loncaric, S. 1997. "Radial basis function-based image segmentation using a receptive field",? Processing of 10th IEEE Symposium on Computer-Based Medical Systems.
  27. Ahmed, M. N. , Yamany, S. M. , Mohamed N. and Moriarty, T. 2002 "A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data",? Proceedings of the IEEE transaction on Medical Images, KY, USA.
Index Terms

Computer Science
Information Sciences


MRI Brain Images Feature Extraction Neural Network Support Vector Machine (SVM) Fuzzy C-Means.