This paper develops an improved (more effective) and safer technology for detecting COVID-19 and thus contributes to the literature and the control of COVID-19. Coronavirus is a new infection that causes the coronavirus ailment called COVID-19. This disease was first found in bat at Wuhan, China, in December 2019. Starting from that time, it has spread rapidly throughout the globe. One of the main identifications of COVID-19 is that it can be handily distinguished by fever. Since this flare-up has begun, 'temperature screening utilizing infrared thermometers and RT-PCR has been utilized in advanced and developed countries to check the warmth of the body to identify the infected person. This is not a very effective way of detection, as it demands huge manpower and infrastructure to go and check one-by-one. Moreover, the close contact between the infected and the person checking can lead to the spread of coronavirus at a faster pace. This paper proposes a framework that can detect the coronavirus instantly and non-invasively from a human cough voice. The proposed framework is much safer as compared to conventional technologies used, as it reduces human interactions to a greater extent. It uses spectrographic images of the voice for COVID detection. This framework has been deployed in a web application to use them from any part of the world without exposing themselves to other infected people. This method encourages non-invasive mechanisms that will prevent from hurting sensitive areas, unlike conventional procedures.

No abstract

No abstract

No abstract