Automatic detection of covid-19 disease using deep-convolution neural networks (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The 2019 novel coronavirus (COVID-19), with a starting point in Wuhan, China, has spreadrapidly among people living in other countries and is approaching approximately 4.5 million casesworldwide according to the statistics of the European Centre for Disease Prevention and Control. Thereare a limited number of COVID-19 test kits available in hospitals due to the increasing number of casesdaily. Therefore, it is necessary to implement an automatic detection system as a quick alternativediagnosis option to prevent COVID-19 from spreading among people. In this study, three differentconvolutional neural network-based models (ResNet50, InceptionV3, and InceptionResNetV2) havebeen proposed for the detection of Coronavirus Pneumonia infected patients using chest X-rayradiographs. ROC analyses and confusion matrices by these three models are given and analyzed using5-fold cross-validation. Considering the performance results obtained, it is seen that the pre-trainedResNet50 model provides the highest classification performance with 98% accuracy among the othertwo 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 theirvalidated X-ray images.