Systematic Analysis of Several Deep Learning Approaches for COVID-19 Detection Using X-ray Images

COVID-19 is a virus-borne malady. A clinical study of infected COVID-19 patients found that most COVID-19 patients suffered lung infection after contracting the disease. Consequently, chest X-rays are a more effective and lower-cost imaging technique for diagnosing lung-related problems. This study used deep learning models, including MobileNetV2,DenseNet201, ResNet50, and VGG19, for COVID-19 prediction. For the study, we used chest X-ray image data for binary classification of COVID-19. 7207 chest X-ray image data were obtained from the Kaggle repository, with 5761 being utilized for training and 1446 being used for validation. A comparative analysis was conducted among the models and examined their accuracy. It has been determined that the DenseNet201 models achieved the highest accuracy of 93.02% for detecting COVID-19 in the lowest compilation time of 27secs. The models, MobileNetV2, ResNet50, and VGG19 had the accuracy rate of 77.28%, 65.86% and 74.92%, respectively. The research indicates that the DenseNet201 model is the most effective in detecting COVID-19 using x-ray imaging.