AUTHOR=Hassan Mohammad Mehedi , AlQahtani Salman A. , Alelaiwi Abdulhameed , Papa João P. 

TITLE=Lightweight neural architectures to improve COVID-19 identification

JOURNAL=Frontiers in Physics

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2023.1153637

DOI=10.3389/fphy.2023.1153637

ISSN=2296-424X

ABSTRACT=The COVID-19 pandemic has had a global impact, transforming the way we manage infectious diseases and interact socially. Researchers from various fields have worked tirelessly to develop vaccines on an unprecedented scale, while different countries have developed various sanitary protocols to deal with more contagious variants. Machine learning-assisted diagnosis has emerged as a powerful tool that can help health professionals deliver faster and more accurate outcomes. However, medical systems that rely on deep learning often require extensive data, which may be impractical for real-world applications. This paper compares lightweight neural architectures for COVID-19 identification, highlighting the strengths and weaknesses of each approach. In addition, we have developed a web tool that accepts chest CT images and outputs the probability of COVID-19 infection, along with a heatmap of the regions used by the intelligent system to make this determination.