Abstract

X-rays imaging is one of the possible method for detecting COVID19. Our research aimed to construct a model using deep learning for detecting COVID-19 pneumonia on high resolution X-rays, relieve working pressure of radiologists and contribute to the control of the epidemic. Methods: For the proposed model development, validation, and testing 260 images available from the repository of GitHub and Kaggle were used. The Images consists of 130 COVID-19 (ignoring SARS, MERS and ARDS) and 130 Normal X-ray images. Findings: The proposed model achieved: sensitivity of 100%, specificity of 100%, accuracy of 100%, PPV of 100%, and NPV of 100% in the dataset. For 260 images, the model achieved a comparable performance to that of expert radiologist. With the assistance of the model, the reading time of the radiologists will greatly be decreased. Conclusion: The deep learning model showed a comparable performance with expert radiologist, and greatly will improve the efficiency of radiologists in clinical practice. It holds great potential to relieve the pressure of frontline radiologists, improve early diagnosis, isolation and treatment, and thus contribute to the control of the epidemic.