Classification of MCA stenosis in diabetes by MLP and RBF neural network

被引：14

作者：

Ergün U. ^{[1
]}

Barýpçý N. ^{[1
]}

Ozan A.T. ^{[2
]}

Serhatlýoolu S. ^{[3
]}

Oǧur E. ^{[3
]}

Hardalaç F. ^{[4
]}

Güler I. ^{[1
]}

机构：

[1] Dept. of Electron. and Comp. Educ., Faculty of Technical Education, Gazi University, Ankara, Turkey

[2] Department of Public Health, Faculty of Medicine, Firat University, Elazig, Turkey

[3] Department of Radiology, Faculty of Medicine, Firat University, Elazig, Turkey

[4] Department of Biophysics, Faculty of Medicine, Firat University, Elazi, Turkey

来源：

Journal of Medical Systems | 2004年 / 28卷 / 5期

关键词：

MCA stenosis; multilayer perceptron neural network; PSD estimation; radial basis function neural network; transcranial doppler;

D O I：

10.1023/B:JOMS.0000041174.34685.5b

中图分类号：

学科分类号：

摘要：

For the classification of Middle Cerebral Artery (MCA) stenosis, Doppler signals have been received from the diabetes and control group by using 2 MHz Transcranial Doppler. After the Fast Fourier Transform (FFT) analyses of the Doppler signals, Power Spectrum Density (PSD) estimations have been made and Multilayer Perceptron (MLP) and Radial Basis Function (RBF) have been dealt to apply to the neural networks. PSD estimations of Doppler signals received from MCA of 104 subjects have been successfully classified by MLP (correct classification = 94.2%) and RBF (correct classification = 88.4%) neural network. As we have seen in the area under ROC curve (AUC), MLP neural network (AUC = 0.934) has classified more successfully when compared with RBF neural network (AUC = 0.873).

引用

页码：475 / 487

页数：12

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