COVID-19 Classification of X-ray Images Using Deep Neural Networks

Objectives

In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts. In this study, we propose a machine learning model for detection of patients tested positive for COVID-19 from CXRs that were collected from inpatients hospitalized in four different hospitals. We additionally present a tool for retrieving similar patients according to the model’s results on their CXRs.

Methods

In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March-August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50, ReNet152, vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image.

Results

Our model achieved accuracy of 90.3%, (95%CI: 86.3%-93.7%) specificity of 90% (95%CI: 84.3%-94%), and sensitivity of 90.5% (95%CI: 85%-94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95%CI: 0.93-0.97).

Conclusion

We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19.

Key Points

• A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%.

• A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model’s image embeddings.