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

Rajaraman, Sivaramakrishnan; Siegelman, Jen; Alderson, Philip O.; Folio, Lucas S.; Folio, Les R.; Antani, Sameer K.

We demonstrate use of iteratively pruned deep learning model ensembles for detecting the coronavirus disease 2019 (COVID-19) infection with chest X-rays (CXRs). The 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 (CNN) and a selection of pretrained CNN models are trained on publicly available CXR collections to learn CXR modality-specific feature representations and the learned knowledge is transferred and fine-tuned to improve performance and generalization in the related task of classifying normal, bacterial pneumonia, and CXRs exhibiting COVID-19 abnormalities. The best performing models are iteratively pruned to identify optimal number of neurons in the convolutional layers 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. The custom and pretrained CNNs are evaluated at the patient-level to alleviate issues due to information leakage and reduce generalization errors. 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 (AUC) of 0.9972 in detecting COVID-19 findings on CXRs as compared to the individual constituent models. 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.