ABSTRACT
Since December 2019, the Coronavirus Disease (COVID-19) pandemic has caused world-wide turmoil in a short period of time, and the infection, caused by SARS-CoV-2, is spreading rapidly. AI-driven tools are used to identify Coronavirus outbreaks as well as forecast their nature of spread, where imaging techniques are widely used, such as CT scans discussed in chapter 6 and chest X-rays. Under the global pandemic of COVID-19, medical and healthcare departments are facing a significant issue in delay of detecting the confirmed cases of COVID-19 infection, the use of artificial intelligence can support to analyze chest X-ray image for detecting the COVID-19 virus. The COVID-19 diagnosis and patient triage is becoming important, because chest X-ray is one of the imaging and reliable technique that plays an important role in the diagnosis of COVID-19 disease. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of chest X-ray data set for deep neural network training is difficult. Several artificial intelligence based system are designed for the automatic detection of COVID-19 using chest X-rays. This chapter will discuss the different approaches used for the detection of COVID-19 and the challenges they are facing. As 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 it's hard to decide whether the pneumonia is caused by COVID-19 or due to any other bacterial or fungal attack. To address this problem, this chapter will cover various artificial intelligence techniques to resolve these problems with a relatively trainable parameters for COVID-19 diagnosis. The chapter is motivated by the fact that X-ray imaging systems are more prevalent and cheaper than CT scan systems.
