Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression
The blood flow hemodynamics of carotid arteries were obtained from carotid arteries of 168 individuals with diabetes using the ultrasound Doppler M-unit. Fast Fourier Transform (FFT) methods were used for feature extraction from the Doppler signals on the timefrequency domain. The parameters, obtained from the Doppler sonograms, were applied to the mathematical models that were constituted to analyze the effect of diabetes on internal carotid artery (ICA) stenosis. In this study, two different mathematical models such as the traditional statistical method based on logistic regression and a Multi-Layer Perceptron (MLP) neural network were used to classify the Doppler parameters. The correct classification of these data was performed by an expert radiologist using angiograpy before they were executed by logistic regression and MLP neural networks. We classified the carotid artery stenosis into two categories such as non-stenosis and stenosis and we achieved similar results (correctly classified (CC)=92.8%) in both mathematical models. But, as the degree of stenosis had been increased to 4 (039%, 4059%, 6079% and 8099% diameter stenosis), it was found that the neural network (CC=73.9%) became more efficient than the logistic regression analysis (CC=67.7%). These outcomes indicate that the Doppler sonograms taken from the carotid arteries may be classified successfully by neural network.
logistic regression mlp neural network ica stenosis doppler ultrasonography blood flow velocity parameters