Superpixel Segmentation-Enabled Transmission Electron Microscopy Images for Rapid and Accurate Detection of Coronavirus

Abstract

Worldwide, SARS-CoV-2 has been responsible for millions of fatalities and extensive disability. Hence, to stop the spread of novel viruses like SARS-CoV-2, Omicron, and other worrying types, rapid and accurate diagnostic techniques are needed to identify symptomatic and asymptomatic carriers as soon as feasible. Early recognition and diagnosis are essential to effective epidemic management. However, different viral strains' shapes and spatial characteristics are similar, complicating image classification, especially in medical virology. This study uses a super-pixels segmentation technique based on transmission electron microscopy (TEM) images to differentiate SARS-CoV-2 from SARS-CoV. This paper aims to develop a method that enables virologists to detect and diagnose viral infections more accurately. In results, SARS-CoV-2 had a median area of 25,145.54 pixels and SARS-CoV of 38,591.35 pixels. The model can help to better understand how viruses develop, spread, diagnose and contain outbreaks. Furthermore, an exceptionally low root mean square error (RMSE) of 0.0275 between the segmentation of the viral area between humans and machines is obtained. Indeed, this low error rate indicates the accuracy of this automated measurement technique. Finally, the developed superpixel segmentation technique provides quick and reliable identification of coronaviruses, promising to significantly contribute to medical virology and help manage epidemics by simplifying prompt viral diagnosis.