Pulmonary Lesion Classification Framework Using the Weighted Ensemble Classification with Random Forest and CNN Models for EBUS Images.

Khomkham, Banphatree; Lipikorn, Rajalida

Khomkham, Banphatree; Lipikorn, Rajalida.

Afiliação

Khomkham B; Machine Intelligence and Multimedia Information Technology Laboratory (MIMIT), Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Lipikorn R; Machine Intelligence and Multimedia Information Technology Laboratory (MIMIT), Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.

Diagnostics (Basel) ; 12(7)2022 Jun 26.

Article em En | MEDLINE | ID: mdl-35885458

Palavras-chave

convolutional neural network (CNN); endobronchial ultrasonography images (EBUS); gray-level co-occurrence matrix (GLCM); pulmonary lesion; radiomics features; random forest; weighted ensemble

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Prognostic_studies Idioma: En Revista: Diagnostics (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Tailândia País de publicação: Suíça

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