Skip to content
2000
Volume 28, Issue 26
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

COVID-19, an infectious disease caused by a newly discovered enveloped virus (SARS-CoV-2), was first reported in Wuhan, China, in December 2019 and affected the whole world. The infected individual may develop symptoms such as high fever, cough, myalgia, lymphopenia, respiratory distress syndrome etc., or remain completely asymptomatic after the incubation period of two to fourteen days. As the virus is transmitted by inhaling infectious respiratory droplets that are produced by sneezing or coughing, so early and rapid diagnosis of the disease can prevent infection and transmission. In the current pandemic situation, the medical industry is looking for new technologies to monitor and control the spread of COVID-19. In this context, the current review article highlights the Artificial Intelligence methods that are playing an effective role in rapid, accurate and early diagnosis of the disease via pattern recognition, machine learning, expert system and fuzzy logic by improving cognitive behavior and reducing human error. Auto-encoder deep learning method, α-satellite, ACEMod and heterogeneous graph auto- encoder are AI approaches that determine the transfer rate of virus and are helpful in shaping public health and planning. In addition, CT scan, X-ray, MRI, and RT-PCR are some of the techniques that are being employed in the identification of COVID-19. We hope using AI techniques; the world can emerge from COVID-19 pandemic while mitigating social and economic crisis.

Loading

Article metrics loading...

/content/journals/cmc/10.2174/0929867328666210106143307
2021-08-01
2025-07-10
Loading full text...

Full text loading...

/content/journals/cmc/10.2174/0929867328666210106143307
Loading

  • Article Type:
    Review Article
Keyword(s): Artificial intelligence; coronavirus; COVID-19; diagnosis; machine learning; pandemic
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test