Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

Abstract

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics-area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

Keywords: COVID-19; X-ray; convolutional neural networks; coronavirus; pneumonia.

Figure 1.

Pre-process of X-ray images. (a) Original chest X-ray image, (b) sharpened image using a Laplacian filter, and (c) average pixel per node (APPN)-applied image (10× enlarged).

Figure 2.

Convolutional neural network 1 (ConvNet#1) architecture with two convolutional and two fully connected layers.

Figure 3.

Highest ROC AUC scores obtained in the COVID-19/Normal and COVID-19/Pneumonia experiments. COVID-19: Coronavirus disease 2019; ROC AUC: receiver operating characteristics–area under the curve.

Figure 4.

Macro-averaged F1 scores of the COVID-19/Normal/Pneumonia experiments. COVID-19: Coronavirus disease 2019.