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

• URL: http://arxiv.org/abs/2201.06052v1
• Date: Sun, 16 Jan 2022 14:12:04 GMT
• Title: Challenges in COVID-19 Chest X-Ray Classification: Problematic Data or Ineffective Approaches?
• Authors: Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarette, Ibrahim Almakky and Mohammad Yaqub
• Abstract summary: deep learning to classify and detect COVID-19 infections from chest radiography images. In this work, we investigate the challenges faced with creating reliable AI solutions from both the data and machine learning perspectives.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 AI 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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