Publication:
Chest x-ray image classification for viral pneumonia and Сovid-19 using neural networks

Дата
2021
Авторы
Efremtsev, V. G.
Efremtsev, N. G.
Teterin, E. P.
Bazavluk, E. S.
Teterin, P. E.
Journal Title
Journal ISSN
Volume Title
Издатель
Научные группы
Организационные подразделения
Организационная единица
Институт ядерной физики и технологий
Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.
Выпуск журнала
Аннотация
© 2021, Institution of Russian Academy of Sciences. All rights reserved.The use of neural networks to detect differences in radiographic images of patients with pneumonia and COVID-19 is demonstrated. For the optimal selection of resize and neural network architecture parameters, hyperparameters, and adaptive image brightness adjustment, precision, re-call, and f1-score metrics are used. The high values of these metrics of classification quality (> 0.91) strongly indicate a reliable difference between radiographic images of patients with pneumonia and patients with COVID-19, which opens up the possibility of creating a model with good predictive ability without involving ready-to-use complex models and without pre-training on third-party data, which is promising for the development of sensitive and reliable COVID-19 ex-press-diagnostic methods.
Описание
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Цитирование
Chest x-ray image classification for viral pneumonia and Сovid-19 using neural networks / Efremtsev, V.G. [et al.] // Computer Optics. - 2021. - 45. - № 1. - P. 149-153. - 10.18287/2412-6179-CO-765
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