Classification of MCA stenosis in diabetes by MLP and RBF neural network.
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).
Ergün
Uyman
U
Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey.
Barýpçý
Necaattin
N
Ozan
Ahmet Tevfik
AT
Serhatlýoğlu
Selami
S
Oğur
Erkin
E
Hardalaç
Firat
F
Güler
Inan
I
D001921:Brain / Q000098:blood supply; D002561:Cerebrovascular Disorders / Q000145:classification*; D048909:Diabetes Complications; D006801:Humans; D020768:Middle Cerebral Artery / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D009415:Nerve Net; D012039:Regional Blood Flow; D014465:Ultrasonics; D014463:Ultrasonography