Comparison of Classification Techniques Applied to Magnetic Resonance Images

Date Issued
2010
Author(s)
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
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