Identification and Detecting COVID-19 from X-Ray Images Using Convolutional Neural Networks

The sudden increase of COVID-19 patients is alarming, and it requires quick diagnosis in a quick time. PCR testing is one of the most used methods to test and diagnose COVID, which is time-consuming. In this paper, we present an end-to-end technique that can detect COVID-19 using chest X-ray scans. We have trained and optimized a convolutional neural network (ConvNet), which was trained on a large COVID-19 dataset. We have performed a series of experiments on a number of different architectures. We have chosen the best performing network architecture and then carried on a series of additional experiments to find the optimal set of hyper-parameters and show and justify a number of data augmentation strategies that have allowed us to enhance our performance on the test set greatly. Our final trained ConvNet has managed to obtain a test accuracy of 97.89%. This high accuracy and very fast test speed can be beneficial to get quick COVID test results for further treatment.