ABSTRACT
The outbreak of COVID-19 has cost the world a lot of lives and causes the shutdown of businesses which get most of the countries gone into economic recession. Despite the fact that some of the vaccines of the pandemic are now available, immediately after the first wave of the COVID-19 pandemic, the second wave of the pandemic has now started and causes a lot of lives and grounds a lot of businesses that have resumed. Therefore, in order to contain its further spread among humans, testing and screening of a large number of suspected COVID-19 cases for appropriate quarantine and treatment measures are of high priority to all governments around the world. However, most of the countries are facing inadequate and standard laboratories for testing a large number of suspected COVID-19 cases in their countries despite the fact that the virus is now endemic like other communicable diseases. Therefore, alternatives in non-medical diagnosis of COVID-19 techniques using artificial intelligence which include deep learning, data mining, machine learning, expert system, software agent, and other techniques are urgently needed in the cause of the diagnosis, containing and combatting the further spread of the pandemic. In this study, deep learning algorithms were used to develop models for predicting COVID-19 using chest x-ray images, and models were able to extract COVID-19 imagery features and provide clinical diagnosis ahead of the pathogenic test with a view to saving time, thereby complementing COVID-19 testing laboratories. ResNet50-based model was found to have the highest accuracy, sensitivity, and AUC score of 99%, 89%, and 96%, respectively. In contrast, EfficientNet B4-based model was found to have the highest specificity of 89%. Therefore, ResNet50-based model which has the highest sensitivity of 89% can be used for diagnosis of COVID-19 infection as well as an adjuvant tool in radiology department in hospitals. © 2022, Springer Nature Switzerland AG.