Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks
Open accessAbstract
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.
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The ResNet50 model can provide the highest classification performance with 98% accuracy among the other two proposed models for detecting coronavirus pneumonia from chest X-ray radiographs.
“The ResNet50 model can provide the highest classification performance with 98% accuracy among the other two proposed models, as confirmed from simulations.”
From Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey (2021) · cites this paper as [105]
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- Authors
- Narin, A., Kaya, C., & Pamuk, Z.
- Affiliations
- Zonguldak Bülent Ecevit University (TR)
- Corresponding author
- Kaya, Ceren
- Venue
- Pattern Analysis and Applications
- Published
- 2021-05-09
- Volume
- 24
- Issue
- 3
- Pages
- 1207-1220
- ISSN
- 1433-7541 (ISSN-L)
- 1433-755X
- PMC
- PMC8106971
- OpenAlex
- W3013507463
- Cited by
- 2,054
- References
- 3
- Citations by year
- 2026: 38 · 2025: 104 · 2024: 192 · 2023: 327 · 2022: 477 · 2021: 630 · 2020: 285 · 2012: 1
- FWCI
- 214.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Pattern Analysis and Applications (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Zonguldak Bülent Ecevit University Institutional Repository (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- COVID-19 detection, chest radiography, coronavirus pneumonia, viral pneumonia, bacterial pneumonia, pre-trained convolutional neural networks, ResNet, InceptionV3, InceptionResNetV2, automated diagnosis, binary classification
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