To decrease patient waiting time for diagnosis of the Coronary Artery Disease, automatic methods are applied to identify its severity using Coronary Computed Tomography Angiography scans or extracted Multiplanar Reconstruction (MPR) images, giving doctors a second-opinion on the priority of each case. The main disadvantage of previous studies is the lack of large set of data that could guarantee their reliability. Another limitation is the usage of handcrafted features requiring manual preprocessing, such as centerline extraction. We overcome both limitations by applying a different automated approach based on ShuffleNet V2 network architecture and testing it on the proposed collected dataset of MPR images, which is bigger than any other used in this field before. We also omit centerline extraction step and train and test our model using whole curved MPR images of 708 and 105 patients, respectively. The model predicts one of three classes: ño stenosis' for normal, ñon-significant' - 1-50% of stenosis detected, 'significant' - more than 50% of stenosis. We demonstrate model's interpretability through visualization of the most important features selected by the network. For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level. Our code is publicly available.
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