White blood cells segmentation and classification using a random forest and residual networks implementation

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorTexas A&M University-
Autor(es): dc.creatorRodrigues Garcia, Marlon-
Autor(es): dc.creatorPonce Ayala, Erika Toneth-
Autor(es): dc.creatorPratavieira, Sebastião-
Autor(es): dc.creatorSalvador Bagnato, Vanderlei-
Data de aceite: dc.date.accessioned2025-08-21T19:10:45Z-
Data de disponibilização: dc.date.available2025-08-21T19:10:45Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1117/12.3007504-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/305308-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/305308-
Descrição: dc.descriptionArtificial intelligence algorithms are interesting solutions to automate the tedious manual counting of white blood cells by a specialist. Although interesting machine learning algorithms have been proposed for this task, there is a lack in the literature for high-accuracy methods (more than 99%) tested on larger datasets (more than 10 thousand images). This paper presents a segmentation and classification methodology, based on Random Forest and ResNet50, along with a comparison between ResNet models with different numbers of layers. The segmentation was tested in microscope-like images mounted using multiple single-cell images, widely available in online datasets, yielding 300×300 images to be classified by the residual network. For image classification, ResNet50 reached higher accuracies (99.3%, to the best of our knowledge, the higher accuracy for models with more than 1000 images), with the model size comparison pointing to model overfitting for larger models.-
Descrição: dc.descriptionSa o Joa o Faculty of Eng. (FESJ) Sa o Paulo State University (UNESP-
Descrição: dc.descriptionSa o Carlos Institute of Physics (IFSC University of Sa o Paulo (USP-
Descrição: dc.descriptionDept. of Biomedical Engineering Texas A&M University-
Descrição: dc.descriptionSa o Joa o Faculty of Eng. (FESJ) Sa o Paulo State University (UNESP-
Idioma: dc.languageen-
Relação: dc.relationProgress in Biomedical Optics and Imaging - Proceedings of SPIE-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectRandom Forest-
Palavras-chave: dc.subjectResidual Networks-
Palavras-chave: dc.subjectSegmentation-
Palavras-chave: dc.subjectWhite blood cell count-
Título: dc.titleWhite blood cells segmentation and classification using a random forest and residual networks implementation-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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