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

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dc.contributor.authorDa Costa Longo, Leonardo Henrique [UNESP]
dc.contributor.authorDo Nascimento, Marcelo Zanchetta
dc.contributor.authorRoberto, Guilherme Freire
dc.contributor.authorMartins, Alessandro S.
dc.contributor.authorDos Santos, Luiz Fernando Segato [UNESP]
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.date.accessioned2023-03-01T21:12:04Z
dc.date.available2023-03-01T21:12:04Z
dc.date.issued2022-01-01
dc.description.abstractIn 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.en
dc.description.affiliationSao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)
dc.description.affiliationFederal University of Uberlândia (UFU) Faculty of Computer Science (FACOM)
dc.description.affiliationFederal Institute of Triângulo Mineiro (IFTM) Federal University of Uberlândia (UFU)
dc.description.affiliationUnespSao Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)
dc.identifierhttp://dx.doi.org/10.1109/IWSSIP55020.2022.9854465
dc.identifier.citationInternational Conference on Systems, Signals, and Image Processing, v. 2022-June.
dc.identifier.doi10.1109/IWSSIP55020.2022.9854465
dc.identifier.issn2157-8702
dc.identifier.issn2157-8672
dc.identifier.scopus2-s2.0-85137162359
dc.identifier.urihttp://hdl.handle.net/11449/241595
dc.language.isoeng
dc.relation.ispartofInternational Conference on Systems, Signals, and Image Processing
dc.sourceScopus
dc.subjectclassifier ensemble
dc.subjectdeep features
dc.subjectfeature ensemble
dc.subjectfractal descriptors
dc.subjectH&E images
dc.titleEnsembles of fractal descriptors with multiple deep learned features for classification of histological imagesen
dc.typeTrabalho apresentado em evento

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Artigos - Ciência da Computação e Estatística - IBILCE