An Evaluation of Transfer Learning With CheXNet on Lung Opacity Detection in COVID-19 and Pneumonia Chest Radiographs
Conference proceedings article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Liu, Andy Wei; Chan, Jonathan H.;
Publisher: MDPI
Publication year: 2021
Start page: 137
End page: 142
Number of pages: 6
ISBN: 9781665443067
ISSN: 20763417
eISSN: 2076-3417
Languages: English-Great Britain (EN-GB)
Abstract
As the COVID-19 pandemic continues to put immense stress on hospitals, healthcare workers, and intensive care units, a quick diagnosis and disease severity assessment for patients is crucial. This would allow clinicians to provide the right treatment early on, and thus, prevent serious illness in patients later. Chest radiography is a fast method of diagnosing patients. By analyzing the presence and distribution of lung opacities in chest radiographs, clinicians can determine the severity of COVID-19 or other pneumonia and apply proper treatment early on. Hence, research into models that can detect such lung opacities in chest radiographs would help clinicians efficiently diagnose. Currently, much research is being done on gathering and classifying chest radiographs. A milestone in this regard has been the development of CheXNet by the Stanford ML group, which claims better performance than radiologists at classifying chest radiographs. In this study, the CheXNet feature extractor backbone is used to test if it can improve the performance of lung opacity object detection models with transfer learning. No improvement in performance was observed on a variety of test datasets, with the models trained using the CheXNet feature extractor experiencing a slight decrease in performance on some test datasets. © 2021 IEEE.
Keywords
CheXNet, Computer Vision, COVID-19, Deep Learning, Object Detection, Transfer Learning
Contact Information