Chest X-ray image classification for viral pneumonia and Сovid-19 using neural networks
| Autor: | E. S. Bazavluk, N. G. Efremtsev, E. P. Teterin, V. G. Efremtsev, P. E. Teterin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
Computer science Radiography convolutional neural network 02 engineering and technology 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine lcsh:Information theory lcsh:QC350-467 Electrical and Electronic Engineering Hyperparameter Artificial neural network business.industry Pattern recognition medicine.disease lcsh:Q350-390 Atomic and Molecular Physics and Optics Computer Science Applications Pneumonia classification covid-19 Viral pneumonia Neural network architecture X ray image 020201 artificial intelligence & image processing Artificial intelligence business x-ray image processing lcsh:Optics. Light |
| Zdroj: | Компьютерная оптика, Vol 45, Iss 1, Pp 149-153 (2021) |
| ISSN: | 2412-6179 0134-2452 |
| Popis: | 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 © 2021, Institution of Russian Academy of Sciences All rights reserved |
| Databáze: | OpenAIRE |
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