Lightweight neural architectures to improve COVID-19 identification - Hassan, Mohammad Mehedi; AlQahtani, Salman A.; Alelaiwi, Abdulhameed; Papa, João P. (Frontiers in Physics, 2023)
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.