Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays

We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestations 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