Towards Improving Magnetic Resonance Image Classification

The main goal of this paper is to propose and investigate a three-stage hierarchical classification scheme. The proposed scheme was examined on the dataset of magnetic resonance images. The experimental results obtained from the classification with the proposed method outperformed the results provided by the flat classification and the two-stage hierarchical classification examined in our previous work. According to this, we can conclude that the proposed method is more suitable for solving MRI classification problem.

[1]  Susan T. Dumais,et al.  Hierarchical classification of Web content , 2000, SIGIR '00.

[2]  Dejan Gjorgjevikj,et al.  HIERARCHICAL CLASSIFICATION OF MAGNETIC RESONANCE IMAGES , 2010 .

[3]  Yu-Chung N. Cheng,et al.  Magnetic Resonance Imaging: Physical Principles and Sequence Design , 1999 .

[4]  Amanda Clare,et al.  Predicting gene function in Saccharomyces cerevisiae , 2003, ECCB.

[5]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.

[6]  Samy Bengio,et al.  Torch: a modular machine learning software library , 2002 .

[7]  D. Selvathi,et al.  Brain MRI Slices Classification Using Least Squares Support Vector Machine , 2007 .

[8]  Dejan Gjorgjevikj,et al.  Classification of magnetic resonance images , 2010, Proceedings of the ITI 2010, 32nd International Conference on Information Technology Interfaces.

[9]  Johan Montagnat,et al.  Automated Estimation of Brain Volume in Multiple Sclerosis with BICCR , 2001, IPMI.

[10]  Dejan Gjorgjevikj,et al.  A Multi-class SVM Classifier Utilizing Binary Decision Tree , 2009, Informatica.

[11]  Chuin-Mu Wang,et al.  Classification for Breast MRI Using Support Vector Machine , 2008, 2008 IEEE 8th International Conference on Computer and Information Technology Workshops.

[12]  Ee-Peng Lim,et al.  Performance measurement framework for hierarchical text classification , 2003, J. Assoc. Inf. Sci. Technol..

[13]  Dejan Gjorgjevikj,et al.  MULTI-CLASS CLASSIFICATION USING SUPPORT VECTOR MACHINES IN BINARY TREE ARCHITECTURE , 2009 .

[14]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[15]  Dejan Gjorgjevikj,et al.  Multi-class classification using support vector machines in decision tree architecture , 2009, IEEE EUROCON 2009.