An Intelligent Dynamic MRI System for Automatic Nasal Tumor Detection
Joint Authors
Liu, Chun-Liang
Huang, Wen-Chen
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-02-14
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Electronic engineering
Medicine
Topics
Abstract EN
Dynamic magnetic resonance images (DMRIs) are one of the major 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.
American Psychological Association (APA)
Huang, Wen-Chen& Liu, Chun-Liang. 2012. An Intelligent Dynamic MRI System for Automatic Nasal Tumor Detection. Advances in Fuzzy Systems،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-459295
Modern Language Association (MLA)
Huang, Wen-Chen& Liu, Chun-Liang. An Intelligent Dynamic MRI System for Automatic Nasal Tumor Detection. Advances in Fuzzy Systems No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-459295
American Medical Association (AMA)
Huang, Wen-Chen& Liu, Chun-Liang. An Intelligent Dynamic MRI System for Automatic Nasal Tumor Detection. Advances in Fuzzy Systems. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-459295
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-459295