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pro vyhledávání: '"Juana Sido"'
Autor:
Yauhen Statsenko, Tetiana Habuza, Tatsiana Talako, Tetiana Kurbatova, Gillian Lylian Simiyu, Darya Smetanina, Juana Sido, Dana Sharif Qandil, Sarah Meribout, Juri G. Gelovani, Klaus Neidl-Van Gorkom, Taleb M. Almansoori, Fatmah Al Zahmi, Tom Loney, Anthony Bedson, Nerissa Naidoo, Alireza Dehdashtian, Milos Ljubisavljevic, Jamal Al Koteesh, Karuna M. Das
Publikováno v:
IEEE Access, Vol 10, Pp 120901-120921 (2022)
Background: Radiomical data are redundant but they might serve as a tool for lung quantitative assessment reflecting disease severity and actual physiological status of COVID-19 patients. Objective: Test the effectiveness of machine learning in elimi
Externí odkaz:
https://doaj.org/article/bcc92725771f46f083dd0cf3d6c43a44
