Classification of the Frequency of Carotid Artery Stenosis with MLP and RBF Neural Networks in Patients with Coroner Artery Disease

Authors:
Hanefi Yý/ldý/rý/m;Hasan Baki Altý/nsoy;Necaattin Barý/pç/ý/;Uç/man Ergü/n;Erkin Oğ/ur;Fı/rat Hardalaç/;İ/nan Gü/ler
Affiliations:
Department of Radiology, Faculty of Medicine, Fı/rat University, Elazig, Turkey/ hanefiyildirim@hotmail.com;Department of Radiology, Faculty of Medicine, Fı/rat University, Elazig, Turkey;Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey;Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey;Department of Radiology, Faculty of Medicine, Fı/rat University, Elazig, Turkey;Department of Biophysics, Faculty of Medicine, Fı/rat University, Elazig, Turkey;Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey
Venue:
Journal of Medical Systems
Year:
2004

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Abstract

For the classification of left and right Internal Carotid Arteries (ICA) stenosis, Doppler signals have been received from the patients with coroner arteries stenosis by using 6.2–8.4 MHz linear transducer. To be able to classify the data obtained from LICA and RICA in artificial intelligence, MLP and RBF neural networks were used. The number of obstructed veins from the coroner angiography, intimal thickness, and plaque formation from the power Doppler US and resistive index values were used as the input data for the neural networks. Our findings demonstrated that 87.5% correct classification rate was obtained from MLP neural network and 80% correct classification rate was obtained from RBF neural network. MLP neural network has classified more successfully when compared with RBF neural network.