Review of Covid-19 infection probability detection based deep learning recognition techniques of chest lungs x-ray images

In global population, the well-being and health of individuals are heavily affected by the Corona virus Disease 2019 (COVID19). Efficient screening regarding the infected patients is considered as one of the essential steps for fighting against the pandemic, with radiology examination with the use of chest radiography being one of the key screening method. Early researches discovered that COVID19 patients have abnormalities in the chest radiography images. The current study assists readers in identifying study starting points in active contour models for COVID19 research that has been specified as a topic of high priority for practitioners and scholars to follow. Additionally, in the case when there were insufficient images, machine learning (ML) methods, like deep learning (DL) approaches can be utilized. For a purpose of training a convolution neural network (CNN), one can use pseudo-coloring approaches as well as a platform to annotate X-ray and computed tomography (CT) images. Strong correlations between the clinical indicators and lesion areas in the images can be found using neural network based regression. Sensitivity of 93.84%, accuracy of 98.50%, specificity of 99.18%, and average receiver operating properties–area under the curve score of 96.51% have been reached as a result of previous work.