Automatic detection of covid-19 disease using deep-convolution neural networks
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Abstract
The 2019 novel coronavirus (COVID-19), with a starting point in Wuhan, China, has spread
rapidly among people living in other countries and is approaching approximately 4.5 million cases
worldwide according to the statistics of the European Centre for Disease Prevention and Control. There
are a limited number of COVID-19 test kits available in hospitals due to the increasing number of cases
daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative
diagnosis option to prevent COVID-19 from spreading among people. In this study, three different
convolutional neural network-based models (ResNet50, InceptionV3, and InceptionResNetV2) have
been proposed for the detection of Coronavirus Pneumonia infected patients using chest X-ray
radiographs. ROC analyses and confusion matrices by these three models are given and analyzed using
5-fold cross-validation. Considering the performance results obtained, it is seen that the pre-trained
ResNet50 model provides the highest classification performance with 98% accuracy among the other
two proposed models (97% accuracy for InceptionV3 and 87% accuracy for Inception-ResNetV2).
The result is based on the data available in the repository of GitHub, Kaggle, and Open-i as per their
validated X-ray images.
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