GL-FusionNet: Fusing global and local features to classify deep and superficial partial thickness burn.

Li, Zhiwei; Huang, Jie; Tong, Xirui; Zhang, Chenbei; Lu, Jianyu; Zhang, Wei; Song, Anping; Ji, Shizhao

Li, Zhiwei; Huang, Jie; Tong, Xirui; Zhang, Chenbei; Lu, Jianyu; Zhang, Wei; Song, Anping; Ji, Shizhao.

Afiliação

Li Z; School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
Huang J; Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China.
Tong X; Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China.
Zhang C; School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
Lu J; Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China.
Zhang W; Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China.
Song A; School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
Ji S; Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, Shanghai 200444, China.

Math Biosci Eng ; 20(6): 10153-10173, 2023 03 29.

Article em En | MEDLINE | ID: mdl-37322927

Assuntos

Queimaduras; Humanos; Queimaduras/diagnóstico

Palavras-chave

burn depth; deep learning; feature fusion; image classification; partial thickness burns

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Queimaduras Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Math Biosci Eng Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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