  This paper proposed an ensemble of deep convolutional neural networks (CNN)
based on EfficientNet, named ECOVNet, to detect COVID-19 using a large chest
X-ray data set. At first, the open-access large chest X-ray collection is
augmented, and then ImageNet pre-trained weights for EfficientNet is
transferred with some customized fine-tuning top layers that are trained,
followed by an ensemble of model snapshots to classify chest X-rays
corresponding to COVID-19, normal, and pneumonia. The predictions of the model
snapshots, which are created during a single training, are combined through two
ensemble strategies, i.e., hard ensemble and soft ensemble to ameliorate
classification performance and generalization in the related task of
classifying chest X-rays.
