Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• IEEE access, 2020-01, Vol.8, p.115041-115050

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2020

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.