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

被引：6

作者：

de Sousa, Orrana L., V ^{[1
]}

Magalhaes, Deborah M., V ^{[2
]}

Vieira, Pablo de A. ^{[1
]}

Silva, Romuere R. V. E. ^{[3
]}

机构：

[1] PPGEE UFPI, Elect Engn, Teresina, Brazil

[2] CSHNB UFPI, Informat Syst, Teresina, Brazil

[3] PPGEE UFPI, CSHNB UFPI, Elect Engn, Informat Syst, Teresina, Brazil

来源：

IEEE LATIN AMERICA TRANSACTIONS | 2021年 / 19卷 / 06期

关键词：

COVID-19; Pulmonary diseases; Deep learning; Coronaviruses; Image segmentation; IEEE transactions; Generative adversarial networks; Deep Learning; diagnosis; survey; Image analysis; CHEST RADIOGRAPHS; DATA AUGMENTATION; LATIN-AMERICA; SUPPORT; IMPACT; GAN;

D O I：

10.1109/TLA.2021.9451237

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：925 / 936

页数：12

共 107 条

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