COMPLEXITY-BASED CLASSIFICATION OF THE CORONAVIRUS DISEASE (COVID-19)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F20%3A00343005" target="_blank" >RIV/68407700:21220/20:00343005 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1142/S0218348X20501145" target="_blank" >https://doi.org/10.1142/S0218348X20501145</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0218348X20501145" target="_blank" >10.1142/S0218348X20501145</a>

Jazyk výsledku

angličtina

Název v původním jazyce

COMPLEXITY-BASED CLASSIFICATION OF THE CORONAVIRUS DISEASE (COVID-19)

Popis výsledku v původním jazyce

COVID-19 is a pandemic disease, which massively affected human lives in more than 200 countries. Caused by the coronavirus SARS-CoV-2, this acute respiratory illness affects the human lungs and can easily spread from person to person. Since the disease heavily affects human lungs, analyzing the X-ray images of the lungs may prove to be a powerful tool for disease investigation. In this research, we use the information contained within the complex structures of X-ray images between the cases of COVID-19 and other respiratory diseases, whereas the case of healthy lungs is taken as the reference point. To analyze X-ray images, we benefit from the concept of Shannon's entropy and fractal theory. Shannon's entropy is directly related to the amount of information contained within the X-ray images in question, whereas fractal theory is used to analyze the complexity of these images. The results, obtained in this study, show that the method of fractal analysis can detect the level of infection among different respiratory diseases and that COVID-19 has the worst effect on the human lungs. In other words, the complexity of X-ray images is proportional to the severity of the respiratory disease. The method of analysis, employed in this study, can be used even further to analyze how COVID-19 progresses in affected patients.

Název v anglickém jazyce

COMPLEXITY-BASED CLASSIFICATION OF THE CORONAVIRUS DISEASE (COVID-19)

Popis výsledku anglicky

COVID-19 is a pandemic disease, which massively affected human lives in more than 200 countries. Caused by the coronavirus SARS-CoV-2, this acute respiratory illness affects the human lungs and can easily spread from person to person. Since the disease heavily affects human lungs, analyzing the X-ray images of the lungs may prove to be a powerful tool for disease investigation. In this research, we use the information contained within the complex structures of X-ray images between the cases of COVID-19 and other respiratory diseases, whereas the case of healthy lungs is taken as the reference point. To analyze X-ray images, we benefit from the concept of Shannon's entropy and fractal theory. Shannon's entropy is directly related to the amount of information contained within the X-ray images in question, whereas fractal theory is used to analyze the complexity of these images. The results, obtained in this study, show that the method of fractal analysis can detect the level of infection among different respiratory diseases and that COVID-19 has the worst effect on the human lungs. In other words, the complexity of X-ray images is proportional to the severity of the respiratory disease. The method of analysis, employed in this study, can be used even further to analyze how COVID-19 progresses in affected patients.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY

ISSN

0218-348X

e-ISSN

1793-6543

Svazek periodika

28

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

000569326300020

EID výsledku v databázi Scopus

2-s2.0-85090010035