Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells.

Go, Taesik; Kim, Jun H; Byeon, Hyeokjun; Lee, Sang J

Go, Taesik; Kim, Jun H; Byeon, Hyeokjun; Lee, Sang J.

Afiliação

Go T; Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
Kim JH; Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
Byeon H; Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
Lee SJ; Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.

J Biophotonics ; 11(9): e201800101, 2018 09.

Article em En | MEDLINE | ID: mdl-29676064

Assuntos

Eritrócitos/patologia; Holografia; Aprendizado de Máquina; Malária/sangue; Humanos; Luz; Espalhamento de Radiação

Palavras-chave

diagnosis; digital holographic microscopy; machine learning algorithm; malaria

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Holografia / Eritrócitos / Aprendizado de Máquina / Malária Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Biophotonics Assunto da revista: BIOFISICA Ano de publicação: 2018 Tipo de documento: Article

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