Machine Intelligence Techniques for the Identification and Diagnosis of COVID-19 - Zaib, Sumera; Rana, Nehal; Noor, Areeba; Khan, Imtiaz (Current Medicinal Chemistry, 2021)
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.
Keywords: machine learning