Automated Grade Classification of Brain Tumor MRI

Alagappa Institute of Skill Development & Computer Centre,Alagappa University, Karaikudi, India.15 -16 February 2017. IT Skills Show & International Conference on Advancements In Computing Resources (SSICACR-2017)

Format: Volume 5, Issue 1, No 24, 2017

Copyright: All Rights Reserved ©2017

Year of Publication: 2017

Author: V.Vani

Reference:IJCS-271

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Abstract

A computer-assisted classification method combining conventional magnetic resonance imaging (MRI) and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including ROI definition, feature extraction, feature selection and classification.The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The extracted features include tumor shape and intensity characteristics as well as rotation invariant texture features. Features subset selection is performed using support vector machines (SVMs) with recursive feature elimination. The binary SVM classification accuracy, sensitivity, and specificity, assessed by leave-one-out cross-validation on 102 brain tumors.

References

[1] K. Arthi & A. Tamilarasi, “Hybrid model in prediction of adhd using artificial neural networks”, International Journal of Information Technology and Knowledge Management , June 2009,vol. 2, no. 1, pp. 209-215 [2] E.A. El-Dahshan, T. Hosny, A.B. M.Salem,“Hybrid intelligent techniques for MRI brain images classification”, Digital Signal Processing, vol.20,issue 2, pp. 433-441, 2010. [3] Qurat-ul-ain ,Ghazanfar Latif, Sidra Batool Kazmi, M.Arfan Jaffar, Anwar M.Mirza, “Classification and Segmentation of Brain Tumor using Texture Analysis”, International Conference on Recent advances in artificial Intelligence,Knowledge Engineering and Databases, pp:147-155, 2010. [4] Lee H., Cho S., Shin M. Supporting Diagnosis of Attentiondeficit Hyperactive Disorder with Novelty Detection. Artificial Intelligence in Medicine, 42, (3), 199–212,2008. [5] M Wang, M. J. Wu, J. H. Chen, C. Y Yu, “Extension Neural Network Approach to Classification of Brain MRI”, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp: 515-517, 2009. [6] M. Varma and B. R. Babu. More generality in efficient multiple kernel learning”,In Proceedings of the International Conference on Machine learning ,Canada ,pp:1065-1072, 2009. [7] Qurat-ul-ain ,Ghazanfar Latif, Sidra Batool Kazmi, M.Arfan Jaffar,Anwar M.Mirza, “Classification and Segmentation of Brain Tumor using Texture Analysis”, International Conference on Recent advances in artificial Intelligence,Knowledge Engineering and Databases, pp:147-155, 2010. [8] Ibrahiem M, Ramakrishnan S., “On the application of various probabilistic neural networks in solving different pattern classification problems”, World Applied Sciences Journal,vol.4,pp:772-780,2008. [9] Kai Xiao, Sooi Hock Ho, Aboul Ella Hassanien, “Brain magnetic resonance image lateral ventricles deformation analysis and tumour prediction”, Malaysian Journal of Computer Science, vol. 20, no.2,pp:115-132 , 2007 [10] S.N.Sivanandam,S.Sumathi,S.N.Deepa, “Introduction to neural networks using Matlab 6”,Tata Mc Graw Hill P Ltd, 2009. [11] M. Varma and B. R. Babu. More generality in efficient multiple kernel learning”,In Proceedings of the International Conference onMachine learning ,Canada ,pp:1065-1072, 2009. [12] C. M Wang, M. J. Wu, J. H. Chen, C. Y Yu, “Extension Neural Network Approach to Classification of Brain MRI”, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp: 515-517, 2009. [13] Mammadagha Mammadov ,Engin tas ,”An improved version of backpropagation algorithm with effective dynamic learning rate and momentum “ ,Inter.Conference on Applied Mathematics ,pp:356-361, 2006. [14] Messen W, Wehrens R,

Keywords

Classification, Feature Extraction, ROI , SVM.

This work is licensed under a Creative Commons Attribution 3.0 Unported License.   

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