Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism.

Budak, Ümit; Çibuk, Musa; Cömert, Zafer; Sengür, Abdulkadir

Budak, Ümit; Çibuk, Musa; Cömert, Zafer; Sengür, Abdulkadir.

Afiliação

Budak Ü; Department of Electrical and Electronics Engineering, Bitlis Eren University, Bitlis, Turkey. ubudak@beu.edu.tr.
Çibuk M; Department of Computer Engineering, Bitlis Eren University, Bitlis, Turkey.
Cömert Z; Department of Software Engineering, Samsun University, Samsun, Turkey.
Sengür A; Department of Electrical-Electronics Engineering, Technology Faculty, Firat University, Elazig, Turkey.

J Digit Imaging ; 34(2): 263-272, 2021 Apr.

Article em En | MEDLINE | ID: mdl-33674979

Assuntos

COVID-19; Humanos; Redes Neurais de Computação; SARS-CoV-2; Semântica; Tomografia Computadorizada por Raios X

Palavras-chave

Attention-based SegNet; COVID-19 segmentation; Convolutional neural network

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Digit Imaging Ano de publicação: 2021 Tipo de documento: Article

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