Abstract
The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic resulting in over 2.7 million infected individuals and over 190,000 deaths and growing. Assertions in the literature suggest that respiratory disorders due to COVID-19 commonly present with pneumonia-like symptoms which are radiologically confirmed as opacities. Radiology serves as an adjunct to the reverse transcription-polymerase chain reaction test for confirmation and evaluating disease progression. While computed tomography (CT) imaging is more specific than chest X-rays (CXR), its use is limited due to cross-contamination concerns. CXR imaging is commonly used in high-demand situations, placing a significant burden on radiology services. The use of artificial intelligence (AI) has been suggested to alleviate this burden. However, there is a dearth of sufficient training data for developing image-based AI tools. We propose increasing training data for recognizing COVID-19 pneumonia opacities using weakly labeled data augmentation. This follows from a hypothesis that the COVID-19 manifestation would be similar to that caused by other viral pathogens affecting the lungs. We expand the training data distribution for supervised learning through the use of weakly labeled CXR images, automatically pooled from publicly available pneumonia datasets, to classify them into those with bacterial or viral pneumonia opacities. Next, we use these selected images in a stage-wise, strategic approach to train convolutional neural network-based algorithms and compare against those trained with non-augmented data. Weakly labeled data augmentation expands the learned feature space in an attempt to encompass variability in unseen test distributions, enhance inter-class discrimination, and reduce the generalization error. Empirical evaluations demonstrate that simple weakly labeled data augmentation (Acc: 0.5555 and Acc: 0.6536) is better than baseline non-augmented training (Acc: 0.2885 and Acc: 0.5028) in identifying COVID-19 manifestations as viral pneumonia. Interestingly, adding COVID-19 CXRs to simple weakly labeled augmented training data significantly improves the performance (Acc: 0.7095 and Acc: 0.8889), suggesting that COVID-19, though viral in origin, creates a uniquely different presentation in CXRs compared with other viral pneumonia manifestations.
Highlights
The novel coronavirus disease 2019 (COVID-19) is caused by a strain of coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that originated in Wuhan in the Hubei province in China
The publicly available data are a curated subset of 26,684 AP and posterior–anterior (PA) chest X-rays (CXR) showing normal and abnormal radiographic patterns, taken from the National Institutes of Health (NIH) CXR-14 dataset [16]. It includes 6012 CXRs showing pneumonia-related opacities with ground truth (GT) bounding box annotations for these on 1241 CXRs; (iii) CheXpert CXR dataset [17]: A subset of 4683 CXRs showing pneumonia-related opacities selected from a collection of 223,648 CXRs in frontal and lateral projections, collected from 65,240 patients at Stanford Hospital, California, and labeled for 14 thoracic diseases by extracting the labels from radiological texts using an automated natural language processing (NLP)-based labeler, conforming to the glossary of the Fleischner Society; (iv) NIH CXR-14 dataset [16]: A subset of 307 CXRs showing pneumonia-related opacities selected from a collection of 112,120 CXRs in frontal projection, collected from 30,805 patients
The VGG-16 model outperformed the others in classifying the pediatric CXRs as showing bacterial or viral pneumonia when considering the F-score and Mathews correlation coefficient (MCC)
Summary
The novel coronavirus disease 2019 (COVID-19) is caused by a strain of coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that originated in Wuhan in the Hubei province in China. The disease is reverse transcription-polymerase chain reaction (RT-PCR). Tests that are shown to exhibit high specificity detected using reverse transcription-polymerase chain reaction (RT-PCR) tests that are shown to but variable sensitivitybut in detecting the presence of the disease [2]. These test are in exhibit high specificity variable sensitivity in detecting the presence of the disease [2]. Limited supply in some geographical regions, third-world countriesthird-world [3] The turnaround these test kits are in limited supply inregions, some geographical countries time is reported to betime
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