AUTOMATED CLASSIFICATION OF BRAIN MRI USING COLORCONVERTED K-MEANS CLUSTERING SEGMENTATION AND APPLICATION OF DIFFERENT KERNEL FUNCTIONS WITH MULTI-CLASS SVM

This paper proposes a hybrid approach for classification of brain magnetic resonance images (MRI) based on color converted hybrid clustering segmentation algorithm and wrapper based feature selection with multi-class support vector machine (SVM). The texture, color and shape features have been extracted and these features are used to classify MR brain images into three categories namely normal, benign and malignant. The MR images are classified by wrapper approach with Multi class Support Vector Machine classifier (MC-SVM) using color, texture and shape features. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate