title: Automated diagnostic systems with diverse and composite features for Doppler ultrasound signals.
abstract: In this paper, we present the automated diagnostic systems for Doppler ultrasound signals classification with diverse and composite features and determine their accuracies. We compared the classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLP), combined neural network (CNN), mixture of experts (ME), modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features. The present study was conducted with the purpose of answering the question of whether the automated diagnostic systems improve the capability of classification of ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals. Our research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems.
authors.affiliation: Gazi University, Faculty of Technical Education, Department of Electronics and Computer Education, 06500 Teknikokullar, Ankara, Turkey.
authors.forename: Inan
authors.initials: I
authors.lastname: Güler
authors.forename: Elif Derya
authors.initials: ED
authors.lastname: Ubeyli
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018608:Ultrasonography, Doppler / Q000379:methods*; D014652:Vascular Diseases / Q000000981:diagnostic imaging*