title: Classification of the frequency of carotid artery stenosis with MLP and RBF neural networks in patients with coroner artery disease.
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.
authors.affiliation: Department of Radiology, Faculty of Medicine, Firat University, Elazig, Turkey. hanefiyildirim@hotmail.com
authors.forename: Hanefi
authors.initials: H
authors.lastname: Yýldýrým
authors.forename: Hasan Baki
authors.initials: HB
authors.lastname: Altýnsoy
authors.forename: Necaattin
authors.initials: N
authors.lastname: Barýpçý
authors.forename: Uçman
authors.initials: U
authors.lastname: Ergün
authors.forename: Erkin
authors.initials: E
authors.lastname: Oğur
authors.forename: l Firat
authors.initials: lF
authors.lastname: Hardalaç
authors.forename: Inan
authors.initials: I
authors.lastname: Güler
mesh_terms: D001185:Artificial Intelligence; D016893:Carotid Stenosis / Q000000981:diagnostic imaging*; D017023:Coronary Angiography / Q000295:instrumentation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012680:Sensitivity and Specificity; D018608:Ultrasonography, Doppler / Q000295:instrumentation / Q000379:methods*