Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review

TL;DR

This paper reviews AI-based methods for early detection of COVID-19 from chest X-ray images, highlighting various deep learning architectures and discussing challenges in distinguishing COVID-19 pneumonia from other types.

Contribution

It provides an overview of different AI approaches used for COVID-19 detection in chest X-rays and discusses the challenges faced in differentiating COVID-19 pneumonia.

Findings

Deep learning models like ResNet, Inception, and GoogLeNet are used for detection.

Current approaches mainly identify pneumonia, not specifically COVID-19.

Distinguishing COVID-19 from other pneumonia causes remains challenging.

Abstract

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.