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Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks.
- Source :
- Pattern Analysis & Applications; Aug2021, Vol. 24 Issue 3, p1207-1220, 14p
- Publication Year :
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14337541
- Volume :
- 24
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Pattern Analysis & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 151687238
- Full Text :
- https://doi.org/10.1007/s10044-021-00984-y