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

Abstract

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.