Comparison of Classification Techniques Applied to Magnetic Resonance Images

Abstract

MRI classification is a very important field of research due to the area of its implementation. The aim of this article is to compare support vector machines (SVM), k-nearest neighbors and C4.5 classifiers when they are applied to MRIs. The dataset used for classification contains magnetic resonance images classified in nine classes. All images of the dataset are described with seven descriptors. The analysis of the classifiers was made for each descriptor separately. According to experimental results we conclude that support vector machines are the most precise and appropriate for the MRI dataset used in this research