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.
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)
M. Ridzuan, A.A. Bawazir, I.G. Navarrete, I. Almakky, and M. Yaqub, "Challenges in COVID-19 chest x-ray classification: problematic data or ineffective approaches?", 2022, arXiv:2201.06052