Automated Classification of MRI Brain Images

The death rate due to brain tumor is increasing rapidly. The accumulation of cells in an uncontrolled manner leads to brain tumor. The life taking tumor can be cured if it is detected in the early stage. This paper deals with analyzing the performance of the different kernels functions on SVM classification algorithm. The performance of the proposed algorithm is being measured by parameters like accuracy, sensitivity, specificity and precision. The kernels like Laplace RBF kernel, Gaussian kernel, polynomial kernel, Hyperbolic tangent kernel, and Sigmoid kernel are applied on SVM. Among the various kernel functions, Gaussian kernel SVM gave good results. Otsu segmentation is applied on the abnormal images to high light the tumor region in the brain MRI images.