Ensembles of fractal descriptors with multiple deep learned features for classification of histological images

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.creatorDa Costa Longo, Leonardo Henrique-
Autor(es): dc.creatorDo Nascimento, Marcelo Zanchetta-
Autor(es): dc.creatorRoberto, Guilherme Freire-
Autor(es): dc.creatorMartins, Alessandro S.-
Autor(es): dc.creatorDos Santos, Luiz Fernando Segato-
Autor(es): dc.creatorNeves, Leandro Alves-
Data de aceite: dc.date.accessioned2025-08-21T20:10:05Z-
Data de disponibilização: dc.date.available2025-08-21T20:10:05Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IWSSIP55020.2022.9854465-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241595-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241595-
Descrição: dc.descriptionIn this paper, we propose an approach to study the ensemble of handcrafted and deep learned features, as well as possible templates for associating them for the classification of histological images. The handcrafted features were calculated with fractal techniques and the deep learned features were extracted from multiple convolutional neural network architectures. The most relevant features from each ensemble, selected with a ranking algorithm, were analyzed by a heterogeneous ensemble of classifiers to avoid overfitting scenarios. The proposed method was applied in the context of histological images of breast cancer, colorectal cancer and liver tissue. The highest accuracies were values from 93.10% to 99.25%. These results allowed defining some standard templates for techniques on different kinds of histological images, for instance, the fractal descriptors when ensembled with deep features via transfer learning can provide the best results. The insights presented here are a relevant contribution to specialists interested in the field of histological images and developing techniques to support the detection and diagnostics of scientifically relevant diseases.-
Descrição: dc.descriptionSao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)-
Descrição: dc.descriptionFederal University of Uberlândia (UFU) Faculty of Computer Science (FACOM)-
Descrição: dc.descriptionFederal Institute of Triângulo Mineiro (IFTM) Federal University of Uberlândia (UFU)-
Descrição: dc.descriptionSao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)-
Idioma: dc.languageen-
Relação: dc.relationInternational Conference on Systems, Signals, and Image Processing-
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Palavras-chave: dc.subjectclassifier ensemble-
Palavras-chave: dc.subjectdeep features-
Palavras-chave: dc.subjectfeature ensemble-
Palavras-chave: dc.subjectfractal descriptors-
Palavras-chave: dc.subjectH&E images-
Título: dc.titleEnsembles of fractal descriptors with multiple deep learned features for classification of histological images-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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