Open AccessA Review of CNN on Medical Imaging to Diagnose COVID-19 Infections
Author(s) -
Susanne Bennett Clark,
Ehsan Kamalinejad,
Christian Magpantay,
Suritaneil Sahota,
Jun Zhong,
Yanke Hu
Publication year - 2021
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 2398-7340
DOI - 10.29007/6xsd
Subject(s) - convolutional neural network , covid-19 , implementation , computer science , field (mathematics) , medical imaging , artificial intelligence , cellular neural network , data science , battle , deep learning , artificial neural network , disease , medicine , pathology , infectious disease (medical specialty) , software engineering , mathematics , archaeology , outbreak , pure mathematics , history
In this paper, we study the Convolutional Neural Network (CNN) applications in medical image processing during the battle against Coronavirus Disease 2019 (COVID- 19). Specifically, three CNN implementations are examined: CNN-LSTM, COVID-Net, and DeTraC. These three methods have been shown to offer promising implications for the future of CNN technology in the medical field. This survey explores how these technologies have improved upon their predecessors. Qualitative and quantitative analyses have strongly suggested that these methods perform significantly better than the commensurate technologies. After analyzing these CNN implementations, it is reasonable to conclude that this technology has a place in the future of the medical field, which can be used by professionals to gain insight into new diseases and to help in diagnosing infections using medical imaging.