About: Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Creator	Shi, Alizadeh, Z Alizadehsani, R Alizadehsani, Roohallah Bazeli, Fahime Ghassemi, Navid Hussain, Sadiq Jafari, Mahboobeh Khadem, Ali Khodatars, M Khodatars, Marjane Khosravi, A Khosravi, Saeid Khozeimeh, Abbas Moridian, Parisa Nahavandi, S Nahavandi, U Rajendra Acharya, Peng Sadeghi, D Sadeghi, Delaram Sani, Javad Shi, P Shoeibi, Afshin Toosi, K Zahra, Alizadeh Zare, Assef
source	ArXiv
abstract	Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed.
has issue date	2020-07-16(xsd:dateTime)
has license	arxiv
sha1sum (hex)	c4bc53414f5c46ccd356a7d74a9cd8b0cfc8b062
resource representing a document's title	Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
resource representing a document's body	covid:c4bc53414f5c46ccd356a7d74a9cd8b0cfc8b062#body_text
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