ABSTRACT
With the rising of the new pandemic, problems to detect the presence of Covid-19 also emerged. To track the infections, RT-PCR and rapid testing are followed in the current situation which is time-consuming and could be an important time for severe patients. To decrease the amount of time for COVID-19 prediction, Chest X-rays could play an important role in determining the result. So by using Chest X-rays with Artificial Intelligence, the COVID-19 disease can be detected in a lesser amount of time under the guidance of the specialist. For this, the Deep Learning techniques like Convolutional Neural Networks (CNN) have been proved quite successful for image recognition and classification. In this experiment, Covid-19 was detected with the help of ResNet architecture whose accuracy increases while going into deeper layers by using skip connections. ResNet is a pre-trained model on the ImageNet database. During the experiment, ResNet18 architecture was used because it has the least number of layers as compared to other CNN architectures and so, for determining the best accuracy obtained with lesser computations. Methods like k-fold cross-validation, confusion matrices, etc were used in obtaining the accuracy of around 89% for COVID-19 prediction. Hence, CNN could be a useful tool for the prediction of COVID-19 and saving time for both patients and doctors for further treatment. © 2021 IEEE.