Abstract
This paper describes an approach based on machine learning technology that is of particular interest for the localization and characterization of both single focal stenoses and multivessel multifocal lesions. Due to the complexity of analyzing large amounts of data for the cardiac surgeon, we pay special attention to the analysis, training, and comparison of popular neural networks that classify and localize foci of stenosis on coronary angiography data. From the complete coronarography dataset collected at the Research Institute for Complex Issues of Cardiovascular Diseases, we retrospectively select data of 100 patients. For the automated analysis of the medical data, the paper considers in detail three models (SSD MobileNet V1, Faster-RCNN ResNet-50 V1, and Faster-RCNN NASNet), which differ in their architecture, complexity, and the number of weights. The models are compared in terms of their basic efficiency characteristics: accuracy, training time, and prediction time. The test results show that the training and prediction times are directly proportional to the complexity of the models. In this regard, Faster-RCNN NASNet exhibits the lowest prediction time (the average processing time for one image is 880 ms), while Faster-RCNN ResNet-50 V1 has the highest prediction accuracy. The latter model reaches the mean average precision (mAP) level of 0.92 on the validation dataset. On the other hand, SSD MobileNet V1 is the fastest model, capable of making predictions with a prediction rate of 23 fps.
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Funding
The work on source data acquisition, preprocessing, and development of the approach for localizing foci of stenosis based on machine learning was supported by the Russian Science Foundation, project no. 18-75-10061 (Research and Implementation of the Concept of Robotic Minimally Invasive Aortic Valve Replacement). The work on training the developed models by using Amazon Web Services was carried out in the framework of the “Nauka” state assignment no. FFSWW-2020-0014 (Development of the Scientific Basis for a Robotic Multiparameter Tomography Technology Based on Big Data Processing and Machine Learning Methods to Investigate Promising Composite Materials). The work on selecting the basic efficiency metrics for the models and their analysis was supported by the Russian Foundation for Basic Research, grant no. 19-07-00351 (Methods and Intelligent Technologies for Scientific Substantiation of Strategic Decisions on Digital Transformation).
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Translated by Yu. Kornienko
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Danilov, V.V., Gerget, O.M., Klyshnikov, K.Y. et al. Analysis of Deep Neural Networks for Detection of Coronary Artery Stenosis. Program Comput Soft 47, 153–160 (2021). https://doi.org/10.1134/S0361768821030038
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DOI: https://doi.org/10.1134/S0361768821030038