MRI brain tumor classification using Support Vector Machines and meta-heuristic method

Abstract

We present a development of a new approach for automated diagnosis, based on classification of Magnetic Resonance (MR) human brain images. 2D Wavelet Transform and Spatial Gray Level Dependence Matrix (DWT-SGLDM) is used for feature extraction. For feature selection Simulated Annealing (SA) is applied to reduce features size. The next step in our approach is Stratified K-fold Cross Validation to avoid overfitting. To optimize support vector machine (SVM) parameters we use Genetic Algorithm and Support Vector Machine (GA-SVM) model. SVM is applied to construct the classifier. An intelligent classification rate of 95,6522% could be achieved using the support vector machine.