ABSTRACT
COVID-19 originated in Wuhan, China, in December 2019, and there have been over 464.5 million infected cases, and 6.08 million individuals have died worldwide. Effective detection of COVID-19 has been an essential task for stopping its quick spread and ultimately saving precious lives. This paper considers radiological examination using chest X-rays as patients with COVID-19 infections are likely to be adequately recognized using chest radiography pictures. Although many machine learning/deep learning techniques have been developed, their approach is likely to suffer problems like generalization error, high variance, overfitting, etc., due to limited dataset size. By producing predictions with numerous models rather than only one model, the ensemble model can overcome the disadvantages of deep learning. So, in this paper, we propose an ensemble deep learning method for detecting COVID-19 using chest X-ray images. On a combination of DenseNet, InceptionV3, and MobileNet, we got the best validation accuracy of 96.20% and testing accuracy of 92.45%. We hope this approach will help detect COVID-19 early and reduce further spread. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.