Reliability of Machine Learning in Eliminating Data Redundancy of Radiomics and Reflecting Pathophysiology in COVID-19 Pneumonia: Impact of CT Reconstruction Kernels on Accuracy

Reliability of Machine Learning in Eliminating Data Redundancy of Radiomics and Reflecting Pathophysiology in COVID-19 Pneumonia: Impact of CT Reconstruction Kernels on Accuracy

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 eliminating data redundancy of radiomics and reflecting...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Blended machine learning model
COVID-19
functional outcomes
lung structural changes
pneumonia
radiomics
Online Access:https://ieeexplore.ieee.org/document/9906085/

Internet

https://ieeexplore.ieee.org/document/9906085/

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