Hierarchical classification of magnetic resonance images
Journal
Sarcoma
Date Issued
2010
Author(s)
Abstract
The objective of the paper is to explore classification on
magnetic resonance images (MRI). In our work on MRI
classification, two types of classification (flat and
hierarchical) are addressed and explored. The
examination is conducted on the dataset of magnetic
resonance images that have hierarchical organization.
All images are described by using Edge histogram
descriptor for the feature extraction process. We
compared the experimental results obtained from the
hierarchical classification to the results provided by flat
classification using different classifiers, such as SVM
methods, k nearest neighbors, C4.5 algorithm and
artificial neural networks. As a result, we concluded
that the hierarchical classification technique
outperforms all other explored classifiers for the
examined dataset of magnetic resonance images.
magnetic resonance images (MRI). In our work on MRI
classification, two types of classification (flat and
hierarchical) are addressed and explored. The
examination is conducted on the dataset of magnetic
resonance images that have hierarchical organization.
All images are described by using Edge histogram
descriptor for the feature extraction process. We
compared the experimental results obtained from the
hierarchical classification to the results provided by flat
classification using different classifiers, such as SVM
methods, k nearest neighbors, C4.5 algorithm and
artificial neural networks. As a result, we concluded
that the hierarchical classification technique
outperforms all other explored classifiers for the
examined dataset of magnetic resonance images.
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