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Automated classification of magnetic resonance brain images using Wavelet Genetic Algorithm and Support Vector Machine

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5 Author(s)
Ahmed Kharrat ; National Engineering School of Sfax, Computer & Embedded Systems Laboratory (CES), B.P 1173, Sfax 3038, Tunisia ; Mohamed Ben Messaoud ; Mohamed Abid ; Karim Gasmi
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In this paper we propose a new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images, using Wavelets Transform (WT) as input to Genetic Algorithm (GA) and Support Vector Machine (SVM). The proposed method segregates MR brain images into normal and abnormal. Our contribution employs genetic algorithm for feature selection witch requires much lighter computational burden. An excellent classification rate of 100% could be achieved using the support vector machine. We observe that our results are significantly better than the results reported in a previous research work employing Wavelet Transform and Support Vector Machine.

Published in:

Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on

Date of Conference:

7-9 July 2010