COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network

Vasilis Nikolaou; Sebastiano Massaro; Masoud Fakhimi; Lampros Stergioulas; Wolfgang Garn

doi:10.1007/s13755-021-00166-4

COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network

Health Inf Sci Syst. 2021 Oct 12;9(1):36. doi: 10.1007/s13755-021-00166-4. eCollection 2021 Dec.

Authors

Vasilis Nikolaou¹, Sebastiano Massaro^{1

2}, Masoud Fakhimi¹, Lampros Stergioulas³, Wolfgang Garn¹

Affiliations

¹ Surrey Business School, University of Surrey, Alexander Fleming Rd, Guildford, GU2 7XH UK.
² The Organizational Neuroscience Laboratory, London, WC1N 3AX UK.
³ The Hague University of Applied Sciences, Johanna Westerdijkplein 75, 2521 EN Den Haag, The Netherlands.

Abstract

Purpose: Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage.

Methods: We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model's performance. From the external test dataset, we calculated the model's accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score.

Results: Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity.

Conclusions: Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19.

Supplementary information: The online version contains supplementary material available at 10.1007/s13755-021-00166-4.

Keywords: Artificial intelligence; COVID-19; Chest x-rays; Classification; Convolution Neural Network; Deep learning.