Classification of lymphomas images with polynomial strategy: An application with Ridge regularization

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorFederal Institute of Triângulo Mineiro (IFTM)-
Autor(es): dc.creatorPereira, Danilo C.-
Autor(es): dc.creatorLongo, Leonardo C.-
Autor(es): dc.creatorTosta, Thaina A. A.-
Autor(es): dc.creatorMartins, Alessandro S.-
Autor(es): dc.creatorSilva, Adriano B.-
Autor(es): dc.creatorFaria, Paulo R. De-
Autor(es): dc.creatorNeves, Leandro A.-
Autor(es): dc.creatorDo Nascimento, Marcelo Z.-
Data de aceite: dc.date.accessioned2025-08-21T20:39:18Z-
Data de disponibilização: dc.date.available2025-08-21T20:39:18Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991780-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248217-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248217-
Descrição: dc.descriptionHistological image analysis through systems to aid diagnosis plays an important role in medicine with supplementary reading for the specialist's diagnosis. This work proposes a method based on the association of extracted features by fractal techniques, regularization and polynomial classifier. The feature vectors were classified by applying the cross-validation technique with 10 folds. The evaluation of the results occurred through metrics such as accuracy (ACC) and imbalance accuracy metric (IAM). The proposed approach achieved significant results for all metrics with non-Hodgkin lymphoma lesion sets. The proposed approach provided values around 0.97 of IAM and 99% of ACC for investigated groups. These results are considered relevant to studies in the literature and the association of Hermite polynomial and regularization can contribute to the detection of the lesions by supporting specialists in clinical practices.-
Descrição: dc.descriptionFederal University of Uberlandia Faculty of Computer Science-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)-
Descrição: dc.descriptionScience and Technology Institute Federal University of São Paulo (UNIFESP)-
Descrição: dc.descriptionFederal Institute of Triângulo Mineiro (IFTM)-
Descrição: dc.descriptionInstitute of Biomedical Science Federal University of Uberlândia (UFU) Department of Histology and Morphology-
Descrição: dc.descriptionSão Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)-
Formato: dc.format258-263-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2022 35th Conference on Graphics, Patterns, and Images, SIBGRAPI 2022-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCAD-
Palavras-chave: dc.subjectHistological Image-
Palavras-chave: dc.subjectPolynomial Classifier-
Palavras-chave: dc.subjectRegularization-
Título: dc.titleClassification of lymphomas images with polynomial strategy: An application with Ridge regularization-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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