Oral Dysplasia Classification by Using Fractal Representation Images and Convolutional Neural Networks

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorFederal Institute of Triangulo Mineiro-
Autor(es): dc.contributorUniversity of Porto-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorCarvalho, Rafael H. O.-
Autor(es): dc.creatorSilva, Adriano B.-
Autor(es): dc.creatorMartins, Alessandro S.-
Autor(es): dc.creatorCardoso, Sérgio V.-
Autor(es): dc.creatorFreire, Guilherme R.-
Autor(es): dc.creatorde Faria, Paulo R.-
Autor(es): dc.creatorLoyola, Adriano M.-
Autor(es): dc.creatorTosta, Thaína A. A.-
Autor(es): dc.creatorNeves, Leandro A.-
Autor(es): dc.creatorDo Nascimento, Marcelo Z.-
Data de aceite: dc.date.accessioned2025-08-21T16:58:56Z-
Data de disponibilização: dc.date.available2025-08-21T16:58:56Z-
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/10.5220/0012389000003660-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/304751-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/304751-
Descrição: dc.descriptionOral cavity lesions can be graded by specialists, a task that is both difficult and subjective. The challenges in defining patterns can lead to inconsistencies in the diagnosis, often due to the color variations on the histological images. The development of computational systems has emerged as an effective approach for aiding specialists in the diagnosis process, with color normalization techniques proving to enhance diagnostic accuracy. There remains an open challenge in understanding the impact of color normalization on the classification of histological tissues representing dysplasia groups. This study presents an approach to classify dysplasia lesions based on ensemble models, fractal representations, and convolutional neural networks (CNN). Additionally, this work evaluates the influence of color normalization in the preprocessing stage. The results obtained with the proposed methodology were analyzed with and without the preprocessing stage. This approach was applied in a dataset composed of 296 histological images categorized into healthy, mild, moderate, and severe oral epithelial dysplasia tissues. The proposed approaches based on the ensemble were evaluated with the cross-validation technique resulting in accuracy rates ranging from 96.1% to 98.5% with the nonnormalized dataset. This approach can be employed as a supplementary tool for clinical applications, aiding specialists in decision-making regarding lesion classification.-
Descrição: dc.descriptionFaculty of Computer Science Federal University of Uberlândia-
Descrição: dc.descriptionFederal Institute of Triangulo Mineiro-
Descrição: dc.descriptionArea of Oral Pathology School of Dentistry Federal University of Uberlândia-
Descrição: dc.descriptionDepartment of Informatics Engineering Faculty of Engineering University of Porto-
Descrição: dc.descriptionDepartment of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia-
Descrição: dc.descriptionScience and Technology Institute Federal University of São Paulo-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) Sao Paulo State University-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) Sao Paulo State University-
Formato: dc.format524-531-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectConvolutional Neural Network-
Palavras-chave: dc.subjectDysplasia-
Palavras-chave: dc.subjectEnsemble-
Palavras-chave: dc.subjectFractal Geometry-
Palavras-chave: dc.subjectHistological Image-
Palavras-chave: dc.subjectReshape-
Título: dc.titleOral Dysplasia Classification by Using Fractal Representation Images and Convolutional Neural Networks-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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