Published Paper Details:

Corona Virus Disease-2019 (COVID-19), which was caused by the severe acute respiratory syndrome-Corona Virus-2 (SARSCoV-2), is a very contagious illness that has killed millions of people all over the world. Imaging methods like computed tomography (CT) and chest x-rays (CXR) are often used to make a quick and accurate COVID-19 diagnosis. Because COVID-19 is a pandemic that is spreading quickly, it must be found quickly in order to stop the virus from spreading. Pictures of the lungs can be used to tell if someone has a corona virus infection. COVID-19 can be found with the help of images from computed tomography (CT) and chest X-rays (CXR). Deep Learning (DL) techniques, and more specifically Convolutional Neural Networks (CNN), have become one of the most popular ways for artificial intelligence (AI) to classify COVID-19. In this work, some of the most important research papers on DL-based categorization of COVID19 using CXR and CT images are looked at, and a summary of those studies is given here. We also give an overview of the latest developments in the field and a critical analysis of the problems that still need to be solved. So that we can come to a conclusion about this work, we will list some possible directions for future research in the COVID-19 imaging classification. The COVID-19 pandemic has quickly spread to all parts of the world. The virus is spreading quickly, which is a danger that makes it harder to stop the disease from spreading. Because of the pandemic, a lot of medical facilities had to switch from giving care in person to doing it over the phone or computer. This is called telemedicine. In order to prevent COVID-19, the goal of this study is to do a literature review on telemedicine applications that use machine learning. Between 2015 and 2022, all of the research that was published in six different electronic databases was looked at in depth. The data that was found during this quick evaluation suggests that machine learning and telemedicine might be able to help stop epidemics by allowing for smart triage of patients and monitoring of their conditions from a distance. There could also be more research and development done on how telemedicine could be used in case of future epidemics.