Classification of Magnetic Resonance brain images by using weighted radial basis function kernels

The paper proposed a weighted Radial basis function kernel (WRBF) approach that can be used to detect and classify anomalies in Magnetic Resonance (MR) images. A weighted Radial basis function kernel (WRBF) approach, despite the fact that the idea of WRBF kernels can be traced back to the work [1], its application to Radial basis function (RBF) kernel is new. It includes the Support Vector Machines (SVMs) using RBF as its special case where the RBF is considered to be uniformly weighted. Methods MR data of abnormal brain data were used to evaluate the accuracy of multiple sclerosis lesions classification by using the proposed method. The data were obtained from the BrainWeb Simulated Brain Database at the McConnell Brain Imaging Centre of the Montreal Neurological Institute (MNI), McGill University. Experimental results via various MR images show that WRBF kernels provide better classification.