Hierarchical classification architectures applied to Magnetic Resonance Images
Date Issued
2011-06-27
Author(s)
Madjarov, Gjorgji
Gjorgjevikj, Dejan
Abstract
The main goal of the paper is to
explore hierarchical classification. The
investigation is performed on the dataset of
Magnetic Resonance Images (MRI) which is
hierarchically organized. Generalized top-down
hierarchical classification architecture is
proposed in the paper. Additionally, two specific
cases of the generalized architecture are
explored: three-stage hierarchical architecture
based on SVM and three-stage hierarchical
architecture based on ANN. From the performed
experiments, it is concluded that the SVM based
scheme outperforms the ANN based scheme.
Moreover, the gain of the investigation
conducted in this paper becomes bigger with the
possibilities given by the proposed generalized
architecture for further investigations.
explore hierarchical classification. The
investigation is performed on the dataset of
Magnetic Resonance Images (MRI) which is
hierarchically organized. Generalized top-down
hierarchical classification architecture is
proposed in the paper. Additionally, two specific
cases of the generalized architecture are
explored: three-stage hierarchical architecture
based on SVM and three-stage hierarchical
architecture based on ANN. From the performed
experiments, it is concluded that the SVM based
scheme outperforms the ANN based scheme.
Moreover, the gain of the investigation
conducted in this paper becomes bigger with the
possibilities given by the proposed generalized
architecture for further investigations.
Subjects
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