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Pattern Analysis and Applications

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09.05.2021 | Theoretical advances

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

verfasst von: Ali Narin, Ceren Kaya, Ziynet Pamuk

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2021

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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.

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Titel: Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks
verfasst von: Ali Narin
Ceren Kaya
Ziynet Pamuk
Publikationsdatum: 09.05.2021
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 3/2021
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-021-00984-y

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