Prediction of COVID-19 Disease and Infection Rate Based on Dense Net

Abstract

Coronavirus is an RNA (Ribonucleic acid) virus in the coronaviridian family that causes zoonotic and infectious diseases transmitted between animals and evolved between humans. This class of pathogens is responsible for respiratory diseases. Coronavirus refers to the crown-like protrusions on the outside surface of the virus. Corona is an infection that causes breathing difficulties in humans. In epidemics, symptomatic techniques based on graphic design are essential for examining the causes of influence, which leads to better results than primary radioscopy mechanisms for identifying and diagnosing COVID-19 cases. The urgent need to employ artificial intelligence in disease detection arose from this standpoint. In this paper, a system is proposed to diagnose infected persons by building a deep learning model and preprocessing processes homogeneously to investigate CT (coronavirus-computed tomography) scan radiographs using the global SARS-Covid dataset, achieving a 99% accuracy rate in diagnosing and identifying COVID-19 or non-COVID-19.