COVID-19 Diagnosis from Medical Images Using Transfer Learning

INTRODUCTION: The novel coronavirus (COVID-19) originated in Wuhan, China, in December 2019. To date, the virus has infected more than 110 million people worldwide and claimed 2.5 million lives. With the rapid increase in the number of infected cases, some countries face a shortage of testing resources. Computational methods such as deep learning algorithms can help in such a situation to expedite and automate the diagnosis of COVID-19. METHODS: In this research, we trained eight convolutional neural network models to automatically detect and diagnose COVID-19 from medical imaging, including X-ray and CT scan images. Those deep learning networks have a predefined structure in which we re-train on medical images to serve our purpose, which is called transfer learning. RESULTS: We used two different medical images known as X-ray and CT scan. The experimental results show that CT scan achieved better performance than X-ray. Specifically, the Xception network model has achieved an overall performance on CT scan of 84%, 91%, and 77% for accuracy, sensitivity, and specificity, respectively. That was the highest in all models that we trained. On the other hand, the same network model (Xception) was applied on X-ray and performed 69%, 83%, and 55% for accuracy, sensitivity, and specificity, respectively. CONCLUSION: The performance of our proposed model to detect COVID-19 from CT scan is acceptable and promising to start in the field. We target the medical sectors to help them by providing rapid and accurate diagnosis of COVID-19 cases using an alternative detection approach to the traditional ones.