NDLI: Covid-19 Image Classification using Deep Learning - Advances, Challenges & Opportunities

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Covid-19 Image Classification using Deep Learning - Advances, Challenges & Opportunities

Content Provider	CMR Institute of Technology, Bengaluru
Author	Fatimah, Binish
Abstract	Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS- CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID- 19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.
ISSN	00104825
Alternate Webpage(s)	http://203.201.63.46:8080/jspui/bitstream/123456789/6662/1/4.%20PAPER4ECE.pdf
Language	English
Publisher	ScienceDirect
Publisher Date	2022-06-01
Access Restriction	Open
Subject Keyword	Problem Analysis Development of Solutions Electronics Communication & Telecommunication Article
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Health Informatics

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