Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT

Abstract The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neur...

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Autores principales:	Edward H. Lee, Jimmy Zheng, Errol Colak, Maryam Mohammadzadeh, Golnaz Houshmand, Nicholas Bevins, Felipe Kitamura, Emre Altinmakas, Eduardo Pontes Reis, Jae-Kwang Kim, Chad Klochko, Michelle Han, Sadegh Moradian, Ali Mohammadzadeh, Hashem Sharifian, Hassan Hashemi, Kavous Firouznia, Hossien Ghanaati, Masoumeh Gity, Hakan Doğan, Hojjat Salehinejad, Henrique Alves, Jayne Seekins, Nitamar Abdala, Çetin Atasoy, Hamidreza Pouraliakbar, Majid Maleki, S. Simon Wong, Kristen W. Yeom
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/b8cdd6ddc1174a5a86416c48a44db3cc
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Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT

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