COVID-19 detection from chest X-ray images using Detectron2 and Faster R-CNN
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570318" target="_blank" >RIV/70883521:28140/23:63570318 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-21438-7_3" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-21438-7_3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-21438-7_3" target="_blank" >10.1007/978-3-031-21438-7_3</a>
Alternative languages
Result language
angličtina
Original language name
COVID-19 detection from chest X-ray images using Detectron2 and Faster R-CNN
Original language description
The COVID-19 outbreak has been causing immense damage to global health and has put the world under tremendous pressure since early 2020. The World Health Organization (WHO) has declared in March 2020 the novel coronavirus outbreak as a global pandemic. Testing of infected patients and early recognition of positive cases is considered a critical step in the fight against COVID-19 to avoid further spreading of this epidemic. As there are no fast and accurate tools available till now for the detection of COVID-19 positive cases, the need for supporting diagnostic tools has increased. Any technological method that can provide rapid and accurate detection will be very useful to medical professionals. However, there are several methods to detect COVID-19 positive cases that are typically performed based on chest X-ray images that contain relevant information about the COVID-19 virus. This paper goal is to introduce a Detectron2 and Faster R-CNN to diagnose COVID-19 automatically from X-ray images. In addition, this study could support non-radiologists with better localization of the disease by visual bounding box. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Networks and System (Volume 597 LNNS)
ISBN
978-3-031-21437-0
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
17
Pages from-to
37-53
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Berlín
Event location
on-line
Event date
Oct 10, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000992418500003