Officials all over the world are using different models to predict the spread of COVID-19 so that they can make well-informed decisions and take the right steps to control it. Authorities are paying more attention to simple epidemiological and statistical models, and they are popular in the media. The traditional models that are used to predict the global pandemic caused by COVID-19 include these models. It has been shown that standard models are not good at making long-term predictions because there is a lot of uncertainty and not enough important data. Even though there are a lot of different ways to solve this problem that have been found in research, the models that are currently being used still need to be improved in how well they can generalize and be resilient. Since the COVID-19 epidemic started, the whole world has seen a level of chaos that has never been seen before. This change has affected every part of our daily lives, including, but not limited to, business, education, transportation, and health care. 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 coronavirus infection. COVID-19 can be found with the help of images from computed tomography (CT) and chest X-rays (CXR). Deep learning algorithms have been shown to work well and do a better job than traditional methods in many applications involving computer vision and medical imaging. As the COVID pandemic continues to spread, researchers are using deep learning techniques to find cases of corona virus infection in lung imaging. In this study, a review of the current deep learning methods used to find coronavirus infections in pictures of the lungs is given. You can find these ways in the section before this one. This paper gives an overview of the different methods that can be used, as well as a list of public datasets, datasets that are used by each method, and evaluation metrics to help researchers in the future. All of the evaluation metrics that are used by the different techniques are compared in detail. Deep learning (DL) is a subfield of applications of artificial intelligence (AI). In the past few years, DL has grown quickly and gained new features that could help in the fight against the COVID-19 pandemic. Using these traits could help with efforts to improve public health. In this study, a deep learning model was also made so that X-ray pictures could be used to predict COVID-19. The model is judged against other models that are already out there, such as ResNet50, DenseNet, and DenseCapsNet. According to these studies, MobileNet has reached a level of accuracy of 98.69%, which is higher than other algorithms that are thought to be cutting-edge.

COVID-19 detection DL-Based COVID-19 detection Lung image classification Coronavirus pandemic Medical image processing