Deep Learning in Image Analysis for COVID-19 Diagnosis: a Survey

COVID-19 achieved the highest concentration of confirmed cases in the Americas with a significant impact in Latin America and the Caribbean region, where access to water and sanitation is restricted. In this scenario, we surveyed deep learning techniques applied to extract information from images to...

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Bibliographic Details
Published in:	Revista IEEE América Latina Vol. 19; no. 6; pp. 925 - 936
Main Authors:	L. V. de Sousa, Orrana, M. V. Magalhaes, Deborah, de A. Vieira, Pablo, Silva, Romuere
Format:	Journal Article
Language:	English
Published:	Los Alamitos IEEE 01-06-2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Computer architecture Coronaviruses COVID-19 Deep learning Diagnosis Generative adversarial networks Image analysis Image segmentation Pulmonary diseases Sanitation Severe acute respiratory syndrome coronavirus 2 survey
Online Access:	Get full text
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Summary:	COVID-19 achieved the highest concentration of confirmed cases in the Americas with a significant impact in Latin America and the Caribbean region, where access to water and sanitation is restricted. In this scenario, we surveyed deep learning techniques applied to extract information from images to detect pneumonia caused by SARS-COV-2, directly assisting health professionals through an automatic case screening. We identify the main public and private image datasets and deep network architectures. Thereby, we identified challenges and research directions. Thus, our goal is to provide a theoretical basis to contribute to the development of computational systems to aid the diagnosis of COVID-19.
ISSN:	1548-0992 1548-0992
DOI:	10.1109/TLA.2021.9451237