Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

• Published in: Pattern analysis and applications : PAA Vol. 24; no. 3; pp. 1207 - 1220
• Main Authors: Narin, Ali, Kaya, Ceren, Pamuk, Ziynet
• Format: Journal Article
• Language: English
• Published: London Springer London 01.08.2021; Springer Nature B.V
• Subjects: Accuracy; Artificial neural networks; Computer Science; Coronaviruses; COVID-19; COVID-19 diagnostic tests; Datasets; Disease transmission; Neural networks; Pattern Recognition; Pneumonia; Radiographs; Theoretical Advances; Viral diseases; Viral pneumonia; Coronavirus; Chest X-ray radiographs; Bacterial pneumonia; Convolutional neural network; Deep transfer learning
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-021-00984-y

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.