Challenges of deep learning diagnosis for COVID-19 from chest imaging

The COVID-19 pandemic has spread worldwide for over 2 years now. The pandemic raises a significant threat to global health due to its transmissibility and high pathogenicity. The current standard detection method for COVID-19, namely, reverse transcription-polymerase chain reaction (RT-PCR), is slow and inaccurate to help fight the pandemic. RT-PCR takes hours to days to report a single test result and has a high false-negative rate. As a result, an infected person with a negative test result may unknowingly continue to spread the virus. Thus, better detection methods are required to improve the control of COVID-19. With technology advancements in artificial intelligence and machine learning, deep-learning diagnostic studies to detect COVID-19 infection using medical chest imaging have emerged. In this paper, we review these studies by analyzing their approaches and highlighting their major challenges. These challenges include dataset cleanness, public dataset availability, capability to differentiate COVID-19 from unrelated viral pneumonia, and the difficulty in dealing with images from multiple points of view. Finally, we discuss various ideas and solutions to address the highlighted challenges in the reviewed papers.

Automated summary

The COVID-19 pandemic has become a global health threat for over 2 years. The current detection method, RT-PCR, is slow and inaccurate. Deep learning diagnostic studies using medical chest imaging have emerged as a better solution. This paper reviews these studies, analyzes their approaches, and discusses ideas and solutions to address the challenges.