Abstract
COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography (CT) or plain chest X-ray (CXR) is sometimes indicated. The value of automated diagnosis is that it saves time and money by minimizing human effort. Three significant contributions are made by our research. Its initial purpose is to use the essential finetuning methodology to test the action and efficiency of a variety of vision models, ranging from Inception to Neural Architecture Search (NAS) networks. Second, by plotting class activation maps (CAMs) for individual networks and assessing classification efficiency with AUC-ROC curves, the behavior of these models is visually analyzed. Finally, stacked ensembles techniques were used to provide greater generalization by combining finetuned models with six ensemble neural networks. Using stacked ensembles, the generalization of the models improved. Furthermore, the ensemble model created by combining all of the finetuned networks obtained a state-of-the-art COVID-19 accuracy detection score of 99.17%. The precision and recall rates were 99.99% and 89.79%, respectively, highlighting the robustness of stacked ensembles. The proposed ensemble approach performed well in the classification of the COVID-19 lesions on CXR according to the experimental results.