Early Prediction of COVID-19 Suspects Based on Fractional Order Optical Flow
5th International Conference on Information Systems and Computer Networks, ISCON 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1759097
ABSTRACT
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. © 2021 IEEE.
COVID-19; CXR image sequence; Fractional derivative; Optical flow; Variational technique; Coronavirus; Optical flows; Textures; Asymptomatic patients; Coronaviruses; Early prediction; Fractional derivatives; Fractional order; Image sequence; Infectious disease; World Health Organization; Variational techniques
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
5th International Conference on Information Systems and Computer Networks, ISCON 2021
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS