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. 2021;24(3):1207-1220.
doi: 10.1007/s10044-021-00984-y. Epub 2021 May 9.

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

Ali Narin  1 , Ceren Kaya  2 , Ziynet Pamuk  2
Affiliations

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

Ali Narin et al. Pattern Anal Appl. 2021.

Abstract

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.

Keywords: Bacterial pneumonia; Chest X-ray radiographs; Convolutional neural network; Coronavirus; Deep transfer learning; Viral pneumonia.

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Conflict of interest statement

Conflicts of interestThe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Representative chest X-ray images of normal (healthy) (first row), COVID-19 (second row), bacterial (third row) and viral pneumonia (fourth row) patients
Fig. 2
Fig. 2
Schematic representation of pre-trained models for the prediction of normal (healthy), COVID-19, bacterial and viral pneumonia patients
Fig. 3
Fig. 3
Visual display of testing and training datasets for five-fold cross-validation
Fig. 4
Fig. 4
Binary Class-1: comparison of training accuracy of 5 different models for fold-4
Fig. 5
Fig. 5
Binary Class-1: comparison of training loss values of 5 different models for fold-4
Fig. 6
Fig. 6
Binary Class-1: comparison of testing accuracy of 5 different models for fold-4
Fig. 7
Fig. 7
Binary Class-2: comparison of training accuracy of 5 different models for fold-4
Fig. 8
Fig. 8
Binary Class-2: comparison of training loss values of 5 different models for fold-4
Fig. 9
Fig. 9
Binary Class-2: comparison of testing accuracy of 5 different models for fold-4
Fig. 10
Fig. 10
Binary Class-3: comparison of training accuracy of 5 different models for fold-4
Fig. 11
Fig. 11
Binary Class-3: comparison of training loss values of 5 different models for fold-4
Fig. 12
Fig. 12
Binary Class-3: comparison of testing accuracy of 5 different models for fold-4
See this image and copyright information in PMC

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