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

Página do item simplificado
dc.contributor.authorPereira, Danilo C.
dc.contributor.authorLongo, Leonardo C. [UNESP]
dc.contributor.authorTosta, Thaina A. A.
dc.contributor.authorMartins, Alessandro S.
dc.contributor.authorSilva, Adriano B.
dc.contributor.authorFaria, Paulo R. De
dc.contributor.authorNeves, Leandro A. [UNESP]
dc.contributor.authorDo Nascimento, Marcelo Z.
dc.contributor.institutionUniversidade Federal de Uberlândia (UFU)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionFederal Institute of Triângulo Mineiro (IFTM)
dc.date.accessioned2023-07-29T13:37:44Z
dc.date.available2023-07-29T13:37:44Z
dc.date.issued2022-01-01
dc.description.abstractHistological 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.en
dc.description.affiliationFederal University of Uberlandia Faculty of Computer Science
dc.description.affiliationSão Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)
dc.description.affiliationScience and Technology Institute Federal University of São Paulo (UNIFESP)
dc.description.affiliationFederal Institute of Triângulo Mineiro (IFTM)
dc.description.affiliationInstitute of Biomedical Science Federal University of Uberlândia (UFU) Department of Histology and Morphology
dc.description.affiliationUnespSão Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)
dc.format.extent258-263
dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991780
dc.identifier.citationProceedings - 2022 35th Conference on Graphics, Patterns, and Images, SIBGRAPI 2022, p. 258-263.
dc.identifier.doi10.1109/SIBGRAPI55357.2022.9991780
dc.identifier.scopus2-s2.0-85146425408
dc.identifier.urihttp://hdl.handle.net/11449/248217
dc.language.isoeng
dc.relation.ispartofProceedings - 2022 35th Conference on Graphics, Patterns, and Images, SIBGRAPI 2022
dc.sourceScopus
dc.subjectCAD
dc.subjectHistological Image
dc.subjectPolynomial Classifier
dc.subjectRegularization
dc.titleClassification of lymphomas images with polynomial strategy: An application with Ridge regularizationen
dc.typeTrabalho apresentado em evento

Arquivos

Coleções

Artigos - Ciência da Computação e Estatística - IBILCE