An intelligent dynamic MRI system for automatic nasal tumor detection

Authors:
Wen-Chen Huang;Chun-Liang Liu
Affiliations:
Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan;Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan
Venue:
Advances in Fuzzy Systems - Special issue on Hybrid Biomedical Intelligent Systems
Year:
2012

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Abstract

Dynamicmagnetic resonance images (DMRIs) are one of themajor tools for diagnosing nasal tumors in recent years. The purpose of this research is to propose a new method to be able to automatically detect tumor region and compare three classifiers' tumor detection performance for DMRI. These three classifiers are AdaBoost, SVM, and Bayes-Gaussian classifier. Three measurable metrics, sensitivity, specificity, accuracy values, match percent, and correspondence ratio, are used for evaluation of each specific classifiers. The experimental results show that SVM has the best sensitivity value, and Bayesian classifier has the best specificity and accuracy values. Moreover, the detected tumor regions that are marked with red color are shown by using each of these three classifiers.