Comparative study of deep learning models for automatic coronary stenosis detection in x-ray angiography

Viacheslav Danilov, Olga Gerget, Kirill Klyshnikov, Evgeny Ovcharenko, Alejandro Frangi

Результат исследований: Материалы для журналарецензирование


The article explores the application of machine learning approach to detect both single-vessel and multivessel coronary artery disease from X-ray angiography. Since the interpretation of coronary angiography images requires interventional cardiologists to have considerable training, our study is aimed at analysing, training, and assessing the potential of the existing object detectors for classifying and detecting coronary artery stenosis using angiographic imaging series. 100 patients who underwent coronary angiography at the Research Institute for Complex Issues of Cardiovascular Diseases were retrospectively enrolled in the study. To automate the medical data analysis, we examined and compared three models (SSD MobileNet V1, Faster-RCNN ResNet-50 V1, Faster-RCNN NASNet) with various architecture, network complexity, and a number of weights. To compare developed deep learning models, we used the mean Average Precision (mAP) metric, training time, and inference time. Testing results show that the training/inference time is directly proportional to the model complexity. Thus, Faster-RCNN NASNet demonstrates the slowest inference time. Its mean inference time per one image made up 880 ms. In terms of accuracy, Faster-RCNN ResNet-50 V1 demonstrates the highest prediction accuracy. This model has reached the mAP metric of 0.92 on the validation dataset. SSD MobileNet V1 has demonstrated the best inference time with the inference rate of 23 frames per second.

Язык оригиналаАнглийский
ЖурналCEUR Workshop Proceedings
СостояниеОпубликовано - 2020
Событие30th International Conference on Computer Graphics and Machine Vision, GraphiCon 2020 - Saint Petersburg, Российская Федерация
Продолжительность: 22 сен 202025 сен 2020

ASJC Scopus subject areas

  • Computer Science(all)

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