  We demonstrate use of iteratively pruned deep learning model ensembles for
detecting pulmonary manifestation of COVID-19 with chest X-rays. This disease
is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2
(SARS-CoV-2) virus, also known as the novel Coronavirus (2019-nCoV). A custom
convolutional neural network and a selection of ImageNet pretrained models are
trained and evaluated at patient-level on publicly available CXR collections to
learn modality-specific feature representations. The learned knowledge is
transferred and fine-tuned to improve performance and generalization in the
related task of classifying CXRs as normal, showing bacterial pneumonia, or
COVID-19-viral abnormalities. The best performing models are iteratively pruned
to reduce complexity and improve memory efficiency. The predictions of the
best-performing pruned models are combined through different ensemble
strategies to improve classification performance. Empirical evaluations
demonstrate that the weighted average of the best-performing pruned models
significantly improves performance resulting in an accuracy of 99.01% and area
under the curve of 0.9972 in detecting COVID-19 findings on CXRs. The combined
use of modality-specific knowledge transfer, iterative model pruning, and
ensemble learning resulted in improved predictions. We expect that this model
can be quickly adopted for COVID-19 screening using chest radiographs.
