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Retracted: COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Detalles Bibliográficos
Autor principal: Methods in Medicine, Computational and Mathematical
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10586393/
https://www.ncbi.nlm.nih.gov/pubmed/37867554
http://dx.doi.org/10.1155/2023/9840132
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author Methods in Medicine, Computational and Mathematical
author_facet Methods in Medicine, Computational and Mathematical
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spelling pubmed-105863932023-10-20 Retracted: COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques Methods in Medicine, Computational and Mathematical Comput Math Methods Med Retraction Hindawi 2023-10-11 /pmc/articles/PMC10586393/ /pubmed/37867554 http://dx.doi.org/10.1155/2023/9840132 Text en Copyright © 2023 Computational and Mathematical Methods in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Retraction
Methods in Medicine, Computational and Mathematical
Retracted: COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques
title Retracted: COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques
title_full Retracted: COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques
title_fullStr Retracted: COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques
title_full_unstemmed Retracted: COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques
title_short Retracted: COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques
title_sort retracted: covid-19 detection based on lung ct scan using deep learning techniques
topic Retraction
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10586393/
https://www.ncbi.nlm.nih.gov/pubmed/37867554
http://dx.doi.org/10.1155/2023/9840132
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