ABSTRACT
The ongoing COVID-19 virus infection has ended up being the biggest pandemic to hit mankind in the last century. It has infected in excess of 50 Million across the globe and has taken in excess of 1.5 Million lives. It has posed problems even to the best healthcare systems across the globe. The best way to reduce the spread and damage of COVID-19 is by early detection of the infection and quarantining the infected patients with necessary medical care. COVID-19 infection can be detected by a chest X-ray. With limited rapid COVID-19 testing kits, this approach of detection with the aid of deep learning can be adopted. The only problem being, the side effects of COVID-19 infection imitate those of conventional Pneumonia, which adds some complexity in utilizing the Chest X-rays for its prediction. In this investigation, we attempt to investigate four approaches i.e., Feature Ensemble, Feature Extraction, Layer Modification and weighted Max voting utilizing State of the Art pre-trained models to accurately identify between COVID-19 Pneumonia, Non-COVID-19 Pneumonia, and Healthy Chest X-ray images. Since very few images of patients with COVID-19 are publicly available, we utilized combinations of image processing and data augmentation methods to build more samples to improve the quality of predictions. Our best model i.e., Modified VGG-16, has achieved an accuracy of 99.5216%. More importantly, this model did not predict a False Negative Normal (i.e., infected case predicted as normal), making it the most attractive feature of the study. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid in controlling it effectively. © 2022 Elsevier Inc. All rights reserved.