Document Type

Article

Publication Title

arXiv

Abstract

The value of quick, accurate, and confident diagnoses cannot be undermined to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we carry out extensive experiments on a large COVID-19 chest X-ray dataset to investigate the challenges faced with creating reliable solutions from both the data and machine learning perspectives. Accordingly, we offer an in-depth discussion into the challenges faced by some widely-used deep learning architectures associated with chest X-Ray COVID-19 classification. Finally, we include some possible directions and considerations to improve the performance of the models and the data for use in clinical settings. © 2022, CC BY-NC-SA.

DOI

doi.org/10.48550/arXiv.2201.06052

Publication Date

1-16-2022

Keywords

Large dataset, Medical imaging, X ray radiography, Chest radiography, Clinical settings, COVID-19, Learning architectures, Learning methods, Performance, Radiography images, Deep learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Machine Learning (cs.LG)

Comments

Preprint: arXiv

Archived with thanks to arXiv

Preprint License: CC BY-NC-SA 4.0

Uploaded 25 March 2022

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