Detection of COVID-19 from chest X-rays using deep transfer learning

Abstract

Objectives: As COVID-19 continues to wreak havoc around the world, effective screening methods for safeguarding public health and bringing the pandemic under control are urgently required. Accordingly, this study proposes a robust pipeline for training and evaluating deep learning predictive models for the detection of COVID-19 from chest X-rays. Methods: The pipeline incorporates multiple techniques to combat overfitting and optimize the prediction results, including data augmentation, Bayesian optimization, the selection of appropriate performance metrics, and the use of early stopping during model training. The proposed pipeline is applied to three common deep learning models: ResNet50, NASNet-A-Mobile, and Xception. Results: The experimental results obtained using the COVID-XRay-5K v3 dataset with 2084 training and 3100 test examples show that the three models achieve AU-PRC (area under precision-recall curve) scores of 0.977, 0.963 and 0.900, respectively; and AU-ROC (area under receiver operating characteristic) scores of 0.994, 0.996 and 0.981. Moreover, at a 98.00% recall (sensitivity), the models achieve high specificities of 97.53%, 97.60% and 86.00%, respectively. Conclusion: Overall, the results suggest that all three models provide a promising approach for the rapid and reliable detection of COVID-19.