Abstract
The 2019 novel coronavirus (COVID-19) originating from China, has spread rapidly among people living in other countries. According to the World Health Organization (WHO), by the end of January, more than 104 million people have been affected by COVID-19, including more than 2 million deaths. The number of COVID-19 test kits available in hospitals is reduced due to the increase in regular cases. Therefore, an automatic detection system should be introduced as a fast, alternative diagnostic to prevent COVID-19 from spreading among humans. For this purpose, three different BiT models: DenseNet, InceptionV3, and Inception-ResNetV4 have been proposed in this analysis for the diagnosis of patients infected with coronavirus pneumonia using X-ray radiographs in the chest. These three models give and examine Receiver Operating Characteristic (ROC) analyses and uncertainty matrices, using 5-fold cross-validation. We have performed the simulations which have visualized that the pre-trained DenseNet model has the best classification efficiency with 92% among two other models proposed (83.47% accuracy for inception V3 and 85.57% accuracy for Inception-ResNetV4).
•COVID-19 prediction using chest X-ray images.•The manual method i.e. PCR is time-consuming and high costs.•A novel automated image-based technique DenseNet and BiTModels like R101x1, which segment and identify Covid-19 patient x-ray.•Covid-19 normal and affected patient data is used.•Comparing with other models, training and testing accuracy, the R101x1 has an average accuracy, i.e., 91% in the Covid-19 class Prediction.