Detection of COVID-19 Enhanced by a Deep Extreme Learning Machine

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has had a tremendous effect on daily life and a great impact on the economy of the world. More than 200 countries have been affected. The diagnosis of coronavirus is a major challenge for medical experts. Early detection is one of the most effective ways to reduce the mortality rate and increase the chance of successful treatment. At this point in time, no antiviral drugs have been approved for use, and clinically approved vaccines have only recently become available in some countries. Hybrid artificial intelligence computer-aided systems for the diagnosis of disease are needed to help prevent the rapid spread of COVID-19. Various detection methods are being used to diagnose coronavirus. Deep extreme learning is the most successful artificial intelligence (AI) technique that efficiently supports medical experts in making smart decisions for the detection of COVID-19. In this study, a novel detection model to diagnose COVID-19 has been introduced to achieve a better accuracy rate. The study focuses on quantitative analysis and disease detection of COVID-19 empowered by a statistical real-time sequential deep extreme learning machine (D2C-RTS-DELM). The experimental results show 98.18% accuracy and 98.87% selectivity, and the probability of detection is 98.84%. The results demonstrate that the quantitative analysis and statistical real-time sequential deep extreme learning machine used in this study perform well in forecasting COVID-19 as well as in making timely decisions for treatment.