Residual Learning Model-Based Classification of COVID-19 Using Chest Radiographs

Abstract

Globally Health crisis has been arising after the flare-up of COVID-19 that enormously affect the routine, how people assess the world and their daily life matters. Further the estimation of illness and the patterns of carrying COVID-19 symptoms threads our sense. RT-PCR Real-Time Polymerase Chain Reaction is regarded as one of the most extensively used procedures for diagnosing Covid-19 illness. This strategy is deemed to be uneconomical in terms of both time and money. Moreover, there are high chances of false negatives in these testing kits. To cope with these issues, radiologists usually employ Chest X-Rays with Pneumonia infection for the early detection of Covid-19 diseases. However, the diagnosis of this disease through manual analysis is considered to be a time-consuming and costly task. So, protection produces took place to control the spread of the COVID-19 virus via social distancing which holds back humans from the thing, which is the natural opposite of human social life. Due to this outbreak, what will be the role of Machine learning in our life in the context of global threats, social distance, and physical as well as mental threats. In this research, there is a considerable need to automate the whole diagnosis task to cope with mentioned problems via the use of pre-trained machine learning algorithms such as Resnet-50.