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Optimized Approach for Brain Tumor Detection from Brain MRI

S. Suresh

Abstract


This paper presents an optimized technique for Brain tumor detection from MR Images. The proposed system consists of two phases, namely, feature extraction and detection. In two steps the efficient techniques are used to increase the accuracy of the system so that to reduce the number of false detection. In feature extraction the texture features are extracted which shows better performance in various diagnoses. The classifiers detect the timorous image based on features. Here the classifier fusion system is used which is formed using more number of classifiers. The K-nn, SVM and ANN classifiers are used in classifier fusion system. The results show that this system has higher efficiency when compared to other system.

Keywords


MRI, Texture, GLCM, Classifier Fusion, K-NN Classifier, ANN, SVM

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References


A.M.Mirza and M.Arfan Jaffer, “Classification and Segmentation of Brain Tumor using Texture Analysis”, Recent advances in artificial intelligence, knowledge engineering and data bases,pp 147-155.

Rafel C.Gonzalez and Richard E.Woods, “Digital Image Processing”,Pearson Education,2008.

Mrs.Mamata S.Kalas,” An Artificial Neural Network for Detection of Biological Early Brain Cancer“International Journal of Computer Applications, Volume 1,pp 17-23 ,2010.

Ludmila I. Kuncheva and Lakhmi C. Jain,” Designing Classifier Fusion Systems by Genetic Algorithms“, IEEE transactions on evolutionary computation, vol. 4, no. 4,pp 327-336, 2000..

El-sayed El-Dahshan,A.M.Salem, and T.H.Younisa”, Hybrid Technique for Automatic MRI brain images Classification, Studia Univ. Babe-Bolyai,Informatica, Volume LIV,pp 55-66,2009.

H. Selvaraj, S. Thamarai Selvi, D. Selvathi and L. Gewali, “Brain MRI Slices Classification Using Least Squares Support Vector Machine”Research Paper, Vol. 1, No. 1, Issue 1, pp 21-33,2007.

R.M. Valdovinos and J.S. Sanchez,” Combining Multiple Classifiers with Dynamic Weighted Voting”, Spanish CICYT (Ministry of Science and Technology)publications,2003

Dipali M. Joshi, Dr.N. K. Rana V. M. Misra,” Classification of Brain Cancer Using Artificial Neural Network”, 2nd International Conference on Electronic Computer Technology (ICECT 2010),pp 112-116,.

P.Rajendran M.Madheswaran ,”An Improved Image Mining Technique For Brain Tumour Classification Using Efficient classifier”,(IJCSIS) International Journal of Computer Science and Information Security,Vol. 6, No. 3,pp 107 116, 2009./

Shubhangi D C and P.S. Hiremath ,”Support Vector Machine (SVM) Classifier for Brain Tumor Detection”, International Conference on Advances in Computing, Communication and Control (ICAC3‟09),pp 444-448,2009.

R. M. Haralick, K. Shanmugam, and I.Dinstein, “Textural Features for Image Classification”, IEEE Trans. on Systems, Man, and Cybernetics, Vol. SMC-3, No.6, November 1973, pp.610-621.

J. Levman, et al, “Classification of dynamic contrast-enhanced magnetic resonance breast lesions by support vector machines”, IEEE Trans. Med. Imaging, Vol. 27 (5), pp.688-696, 2008;

El-Sayed A. El-Dahshan, Abdel-Badeeh M. Salem, And Tamer H. Younis” A Hybrid Technique For Automatic Mri Brain Images Classification” TUDIA UNIV. BABES_{BOLYAI, INFORMATICA, Volume LIV, Number 1, 2009

H. Selvaraj, S. Thamarai Selvi, D. Selvathi, L. Gewali,” Brain MRI Slices Classification Using Least Squares Support Vector Machine”, International Journal of Intelligent Computing in Medical Sciences and Image processing, Vol. 1, No. 1, Issue 1, 2007.




DOI: http://dx.doi.org/10.36039/AA062011005

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