Abstract

The novel Corona Virus (COVID-19) has become the reason for the world to declare it as a global pandemic, which has already taken many lives from all around the world. This pandemic has become a disaster since the spreading rate from person to person is incredibly high and many techniques have come forth to aid in stopping the infection. Although various types of methods have been put into implementation, the search and suggestions of new approaches to reduce the increasing rate of infection will never come to an end until a vaccine terminates this pandemic. This study focuses on proposing a new framework that is based on Deep Learning algorithms for recognizing the COVID-19 cases, mostly in public places. The algorithms include Background Subtraction for extracting the foreground of thermal images from thermal videos generated by Thermal Cameras through the Thermal Imaging process and the Convolutional Neural Network for detecting people infected with the virus. This automated prototype works in a real-time scenario that helps identify people with the disease and will try to trace it while separating them from having any other contact. This proposal intends to achieve a satisfying growth in determining the real cases of COVID-19 and minimize the spreading rate of this virus to the max, ultimately avoiding more deaths.

MeSH

Algorithms, Covid-19, Viruses, Pandemics, Infections

Cited by

• Epidemic model dynamics and fuzzy neural-network optimal control with impulsive traveling and migrating: Case study of COVID-19 vaccination. Biomed Signal Process Control, 2021