Deep Learning Based COVID-19 Detection from Chest X-ray Images

The Coronavirus Disease 2019 (COVID-19) pandemic has dramatically affected the healthcare systems and economy over the world. To help reduce the negative effects of such a contagious malady, the detection time of COVID-19 becomes crucial. While one of the most promising methods to detect whether a patient is infected with this virus is to track his or her lung tissue with chest x-ray (CXR) images, it is considerably time-consuming if all the images are examined by professional radiologists. Besides, the accuracy of detection is determined mainly by the status and stress of the specific radiologist. To tackle these issues, we present a convolutional neural network that automatically detects infections from CXR images. Due to the limited amount of training data, data augmentation methods are used to reduce overfitting. Besides, training improvements such as warmup, cosine scheduler, and label smoothing are also applied during training. The proposed method is validated on the COVID-QU-Ex dataset and has an accuracy of 95.18% and an F1 score of 95.11 % on the validation set, showing its powerful potential in real-world applications for medical diagnostic systems.