COVID-19 detection from chest X-ray images using Detectron2 and Faster R-CNN

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

COVID-19 detection from chest X-ray images using Detectron2 and Faster R-CNN

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

COVID-19 detection from chest X-ray images using Detectron2 and Faster R-CNN

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Networks and System (Volume 597 LNNS)

ISBN

978-3-031-21437-0

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

17

Strana od-do

37-53

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

on-line

Datum konání akce

10. 10. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000992418500003