| Περίληψη: | Cardiovascular Diseases, which the Coronary Artery Disease is part of, are the leading cause of death worldwide, despite the progress made in prognosis and treatment. Accurate, noninvasive diagnosis of the disease is impossible to achieve, due to the constrained accuracy of the diagnostic tests and the complexity of the parameters affecting the risk of suffering from the disease. Hence, humans are forced to undergo the invasive way of diagnosis and treatment, the Coronary Angiography. It is proven that 30% to 50% of the candidates that undergo the Coronary Angiography were healthy. This is the reason why a lot of research has been going on regarding the prognosis and the automatic diagnosis of CAD. Recently, several approaches have been employed, reclaiming the advances in Data Mining and Machine Learning of the past years. In this work, we approach the problem with Fuzzy Modelling, Machine Learning, and Deep Learning approaches. Based on the Diploma Thesis for the BSc Degree of Electrical and Computer Engineering, an improved method of modelling Coronary Artery Disease with Fuzzy Cognitive Maps is presented in this thesis. Next, Machine Learning and Deep Learning methods are examined. For Machine Learning, state-of the art classifiers are employed to perform the diagnosis. Utilizing the Myocardial Perfusion images from the database, Deep Learning with Convolutional Neural Networks is examined for the classification of the medical images. The state-of-the-art Convolutional Neural Network, called Virtual Geometry Group (VGG) was employed to perform the classification task. The three methods are compared, and conclusions regarding their advantages and drawbacks are discussed.
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