DocumentCode :
760472
Title :
Automated Diagnostic Systems With Diverse and Composite Features for Doppler Ultrasound Signals
Author :
Guler, I. ; Ubeyli, E.D.
Author_Institution :
Dept. of Electron.-Comput. Educ., Gazi Univ.
Volume :
53
Issue :
10
fYear :
2006
Firstpage :
1934
Lastpage :
1942
Abstract :
In this paper, we present the automated diagnostic systems for Doppler ultrasound signals classification with diverse and composite features and determine their accuracies. We compared the classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLP), combined neural network (CNN), mixture of experts (ME), modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features. The present study was conducted with the purpose of answering the question of whether the automated diagnostic systems improve the capability of classification of ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals. Our research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems
Keywords :
Doppler measurement; biomedical ultrasonics; blood vessels; eye; medical signal processing; multilayer perceptrons; signal classification; support vector machines; Doppler Ultrasound signals; automated diagnostic systems; combined neural network; composite features; diverse features; internal carotid arterial Doppler signals; mixture of experts; modified mixture of experts; multilayer perceptron neural network; ophthalmic arterial Doppler signals; probabilistic neural network; signal classification; support vector machine; Cellular neural networks; Diseases; Feature extraction; Independent component analysis; Multi-layer neural network; Multilayer perceptrons; Neural networks; Support vector machine classification; Support vector machines; Ultrasonic imaging; Combined neural network (CNN); Doppler ultrasound signals; composite feature; diverse features; mixture of experts (ME); modified mixture of experts (MME); multilayer perceptron neural network (MLP); probabilistic neural network (PNN); support vector machine (SVM); Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Ultrasonography, Doppler; Vascular Diseases;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2005.863929
Filename :
1703744
Link To Document :
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