Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/42737
Title: Role of Artificial Intelligence in COVID-19 Detection
Contributor(s): Gudigar, Anjan (author); Raghavendra, U (author); Nayak, Sneha (author); Ooi, Chui Ping (author); Chan, Wai Yee (author); Gangavarapu, Mokshagna Rohit (author); Dharmik, Chinmay (author); Samanth, Jyothi (author); Kadri, Nahrizul Adib (author); Hasikin, Khairunnisa (author); Barua, Prabal Datta (author); Chakraborty, Subrata  (author)orcid ; Ciaccio, Edward J (author); Acharya, U Rajendra (author)
Publication Date: 2021-12
Early Online Version: 2021-12-01
Open Access: Yes
DOI: 10.3390/s21238045
Handle Link: https://hdl.handle.net/1959.11/42737
Abstract: 

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

Publication Type: Journal Article
Source of Publication: Sensors, 21(23), p. 1-39
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 1424-8220
1424-8239
Fields of Research (FoR) 2020: 460102 Applications in health
461103 Deep learning
460308 Pattern recognition
Socio-Economic Objective (SEO) 2020: 209999 Other health not elsewhere classified
280115 Expanding knowledge in the information and computing sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

Files in This Item:
2 files
File Description SizeFormat 
openpublished/RoleChakraborty2021JournalArticle.pdfPublished version4.04 MBAdobe PDF
Download Adobe
View/Open
Show full item record

SCOPUSTM   
Citations

38
checked on Oct 12, 2024

Page view(s)

972
checked on Mar 7, 2023

Download(s)

8
checked on Mar 7, 2023
Google Media

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons