Challenges in COVID-19 Chest X-Ray Classification: Problematic Data or Ineffective Approaches?

Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub

Keywords: COVID-19, radiology, X-ray, classification

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.

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Paper Type: validation/application paper

Primary Subject Area: Detection and Diagnosis

Secondary Subject Area: Application: Radiology

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