Early Prediction of COVID-19 Suspects Based on Fractional Order Optical Flow

Novel Coronavirus disease (COVID-19) is an infectious disease that has been declared as a pandemic by the World Health Organization. Both symptomatic, as well as asymptomatic patients, are equally likely capable of spreading the virus among the population. Therefore, a real-time tracing of COVID-19 suspects and their identification by a computer-based algorithm is a need of the current times, so that the spreaders could be isolated and the mushrooming should be halted. In this paper, we introduced a fractional order variational model for the early prediction and detection of COVID-19 suspects based on the CXR image sequence. The identification is performed in terms of optical flow color map. The proposed technique would be financially cheaper, require less time and manpower in comparison to the available techniques. The presented model keeps discontinuous information about texture and edges and offers a dense flow field for minuscule variations. The Grünwald-Letnikov derivative is employed for discretizing the complex fractional order partial derivatives. The validity of the model is verified through a variety of experimental results on various datasets.