Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F20%3A43959116" target="_blank" >RIV/49777513:23220/20:43959116 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/9115663" target="_blank" >https://ieeexplore.ieee.org/abstract/document/9115663</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2020.3001973" target="_blank" >10.1109/ACCESS.2020.3001973</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment

Popis výsledku v původním jazyce

COVID-19 outbreak has put the whole world in an unprecedented difcult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19&apos;s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Articial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Articial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

Název v anglickém jazyce

Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment

Popis výsledku anglicky

COVID-19 outbreak has put the whole world in an unprecedented difcult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19&apos;s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Articial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Articial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF18_069%2F0009855" target="_blank" >EF18_069/0009855: Elektrotechnické technologie s vysokým podílem vestavěné inteligence</a><br>

Návaznosti

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

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

8

Číslo periodika v rámci svazku

2020

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

109581-109595

Kód UT WoS článku

000546414000007

EID výsledku v databázi Scopus

2-s2.0-85087668610