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Ali Narin, Ceren Kaya, Ziynet Pamuk
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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Narin, A., Kaya, C., & Pamuk, Z. (2021). Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24(3), 1207–1220. https://doi.org/10.1007/s10044-021-00984-y
Narin, Ali, et al. "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks." Pattern Analysis and Applications, vol. 24, no. 3, 2021, pp. 1207–1220, https://doi.org/10.1007/s10044-021-00984-y.
Narin, Ali, Ceren Kaya, and Ziynet Pamuk. "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks." Pattern Analysis and Applications 24, no. 3 (2021): 1207–1220. https://doi.org/10.1007/s10044-021-00984-y.
Narin A., Kaya C., Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks // Pattern Analysis and Applications. — 2021. — Vol. 24, № 3. — P. 1207–1220. — URL: https://doi.org/10.1007/s10044-021-00984-y
@article{narin2021automatic,
title = {{Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks}},
author = {Ali Narin and Ceren Kaya and Ziynet Pamuk},
journal = {Pattern Analysis and Applications},
year = {2021},
volume = {24},
number = {3},
pages = {1207--1220},
doi = {10.1007/s10044-021-00984-y},
url = {https://doi.org/10.1007/s10044-021-00984-y}
}TY - JOUR AU - Narin, Ali AU - Kaya, Ceren AU - Pamuk, Ziynet TI - Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks JO - Pattern Analysis and Applications VL - 24 IS - 3 SP - 1207 EP - 1220 PY - 2021 DA - 2021/05/09 DO - 10.1007/s10044-021-00984-y UR - https://doi.org/10.1007/s10044-021-00984-y ER -
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