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Application of Adaptive Resonance Theory Neural Network for MR Brain Tumor Image Classification

Application of Adaptive Resonance Theory Neural Network for MR Brain Tumor Image Classification

D. Jude Hemanth, D. Selvathi, J. Anitha
Copyright: © 2010 |Volume: 5 |Issue: 1 |Pages: 15
ISSN: 1555-3396|EISSN: 1555-340X|ISSN: 1555-3396|EISBN13: 9781616929299|EISSN: 1555-340X|DOI: 10.4018/jhisi.2010110304
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MLA

Jude Hemanth, D., et al. "Application of Adaptive Resonance Theory Neural Network for MR Brain Tumor Image Classification." IJHISI vol.5, no.1 2010: pp.61-75. http://doi.org/10.4018/jhisi.2010110304

APA

Jude Hemanth, D., Selvathi, D., & Anitha, J. (2010). Application of Adaptive Resonance Theory Neural Network for MR Brain Tumor Image Classification. International Journal of Healthcare Information Systems and Informatics (IJHISI), 5(1), 61-75. http://doi.org/10.4018/jhisi.2010110304

Chicago

Jude Hemanth, D., D. Selvathi, and J. Anitha. "Application of Adaptive Resonance Theory Neural Network for MR Brain Tumor Image Classification," International Journal of Healthcare Information Systems and Informatics (IJHISI) 5, no.1: 61-75. http://doi.org/10.4018/jhisi.2010110304

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Abstract

In the present study, the effectiveness of the adaptive resonance theory neural network (ART2) is illustrated in the context of automatic classification of abnormal brain tumor images. Abnormal images from four different classes namely metastase, meningioma, glioma and astrocytoma have been used in this work. Initially, textural features are extracted from these images. An extensive feature selection is performed to optimize the number of features. These optimized features are then used to classify the images using ART2 neural network. Experimental results show promising results for the ART2 network in terms of classification accuracy and convergence rate. A comparison is made with other conventional classifiers to show the superior nature of ART2 neural network. The classification accuracy of the ART2 classifier is significantly higher than the statistical classifiers. ART2 classifier is also computationally feasible over other neural classifiers. Thus this work suggests ART2 neural network as an optimal image classifier which finds application in clinical field.

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