Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review
It addresses the urgent need for automatic detection systems to prevent virus transmission, but is incremental as it reviews existing approaches without presenting new results.
This early review discusses the use of artificial intelligence, particularly deep learning architectures like ResNet and Inception, for detecting COVID-19 from chest X-ray images, highlighting challenges such as distinguishing COVID-19 pneumonia from other causes.
In 2019, the entire world is facing a situation of health emergency due to a newly emerged coronavirus (COVID-19). Almost 196 countries are affected by covid-19, while USA, Italy, China, Spain, Iran, and France have the maximum active cases of COVID-19. The issues, medical and healthcare departments are facing in delay of detecting the COVID-19. Several artificial intelligence based system are designed for the automatic detection of COVID-19 using chest x-rays. In this article we will discuss the different approaches used for the detection of COVID-19 and the challenges we are facing. It is mandatory to develop an automatic detection system to prevent the transfer of the virus through contact. Several deep learning architecture are deployed for the detection of COVID-19 such as ResNet, Inception, Googlenet etc. All these approaches are detecting the subjects suffering with pneumonia while its hard to decide whether the pneumonia is caused by COVID-19 or due to any other bacterial or fungal attack.