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Abstract

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


Rajaraman S, Siegelman J, Alderson PO, Folio LS, Folio LR, Antani SK

2020, [Online].

Abstract:

We demonstrate use of iteratively pruned deep learning (DL) 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 COVID19 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 bestperforming 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.


Rajaraman S, Siegelman J, Alderson PO, Folio LS, Folio LR, Antani SK Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays. 
2020, [Online].

URL: https://arxiv.org/abs/2004.08379