The aim of the paper is to compare
classification error of the classifiers applied to
magnetic resonance images for each descriptor
used for feature extraction. We compared several
Support Vector Machine (SVM) techniques,
neural networks and k nearest neighbor
classifier for classification of Magnetic
Resonance Images (MRIs). Different descriptors
are applied to provide feature extraction from
the images. The dataset used for classification
contains magnetic resonance images classified in
9 classes.
