A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU139271" target="_blank" >RIV/00216305:26220/21:PU139271 - isvavai.cz</a>

Result on the web

<a href="https://authors.elsevier.com/c/1cWTplQOv9Sza" target="_blank" >https://authors.elsevier.com/c/1cWTplQOv9Sza</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bbe.2021.01.002" target="_blank" >10.1016/j.bbe.2021.01.002</a>

Result language

angličtina

Original language name

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

Original language description

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30303 - Infectious Diseases

Project

<a href="/en/project/VI04000039" target="_blank" >VI04000039: Early COVID-19 infection detection system for the safety of vulnerable groups using artificial intelligence</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BIOCYBERN BIOMED ENG

ISSN

0208-5216

e-ISSN

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Volume of the periodical

41

Issue of the periodical within the volume

1

Country of publishing house

PL - POLAND

Number of pages

16

Pages from-to

1-16

UT code for WoS article

000643728600016

EID of the result in the Scopus database

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