CORONA-19 NET: Transfer Learning Approach for Automatic Classification of Coronavirus Infections in Chest Radiographs

Coronavirus disease has been rampaging the world since its onset in the Wuhan region of China with cases skyrocketing every day. A crucial step for mitigating the havoc in this situation is the early screening of the infected patients and isolating them. Given the overwhelming number of people falling prey to this pandemic, it becomes very difficult for hospitals to provide these services within the required time. The power of Artificial Intelligence can be leveraged to solve the issue of delay in the diagnosis of coronavirus by automating the process. In this study, we propose our method, a transfer learning approach on deep convolutional neural networks to classify chest radiographs of patients as healthy or infected with Coronavirus based on the fact that Coronavirus attacks the epithelial cells of lungs resulting in pneumonia. The data used for this study has been curated from various public resources online. Our proposed model was able to achieve a validation score of 100% on the curated dataset along with high specificity and sensitivity of 1.00. This work aims to serve as a first cut solution to the automated diagnosis and doesn’t claim to be the exact solution without further validation by medical professionals.