Improving Coronavirus (COVID-19) Diagnosis Using Deep Transfer Learning

Background: Coronavirus disease (COVID-19) is an infectious dis- ease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world. Aim: The aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia, and healthy cases using deep learning techniques. Method: In this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance of different CNN architectures. Results: Evaluation results using K-fold (10) showed that we have achieved state-of-the-art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole. Conclusion: Quantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models developed in this study could be used for early screening of coronavirus; however, it calls for extensive need to CT or X-rays dataset to develop a reliable application.