Computational analysis of histological images from hematoxylin and eosin-stained oral epithelial dysplasia tissue sections

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorFederal Institute of Triângulo Mineiro (IFTM)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Uberaba (UNIUBE)-
Autor(es): dc.creatorSilva, Adriano Barbosa-
Autor(es): dc.creatorMartins, Alessandro Santana-
Autor(es): dc.creatorTosta, Thaína Aparecida Azevedo-
Autor(es): dc.creatorNeves, Leandro Alves-
Autor(es): dc.creatorServato, João Paulo Silva-
Autor(es): dc.creatorde Araújo, Marcelo Sivieri-
Autor(es): dc.creatorde Faria, Paulo Rogério-
Autor(es): dc.creatordo Nascimento, Marcelo Zanchetta-
Data de aceite: dc.date.accessioned2025-08-21T20:00:34Z-
Data de disponibilização: dc.date.available2025-08-21T20:00:34Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2021.116456-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/230237-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/230237-
Descrição: dc.descriptionOral epithelial dysplasia is a precancerous lesion that presents alterations in the shape and size of cell nuclei and can be graded as mild, moderate and severe. The conventional process for diagnosis of this lesion is complex, time-consuming and subject to errors. The use of digital systems in histological analysis can aid specialists to obtain data that allows a robust and fast investigation of the lesion. This work presents a method for dysplasia quantification in histopathological images of the oral cavity using machine learning models. The methodology includes the steps of nuclei segmentation, post-processing, feature extraction and classification. On the segmentation step, the Mask R-CNN neural network was trained using nuclei masks, where objects were detected. The post-processing step employed morphological operations to remove false positive and negative areas. Then, 23 morphological and non-morphological features such as area, orientation, solidity and entropy were computed and a polynomial classifier was employed to distinguish the images among the lesion's grades. This approach was applied in a dataset with 296 regions of mice tongue images, where 9155 cell nuclei were identified and analysed. Metrics such as accuracy and area under the ROC curve were employed to evaluate the methodology by comparing it with the gold standard marked by specialists and other methods present in the literature. This work presents a novel study for the classification of automated grading of oral dysplasia lesions based on the association of CNN segmentation and polynomial algorithm. The segmentation step resulted in accuracies ranging from 88.92% to 90.35% and the classification step obtained area under the ROC curve ranging from 0.88 to 0.97. When compared to other algorithms present in the literature, our methods showed more relevant results, obtaining higher accuracy and AUC values. These values showed that the proposed methodology contributed to the state-of-the-art and can be used as a tool to aid pathologists with precise values for investigating dysplastic tissue lesions.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)-
Descrição: dc.descriptionFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121, BLB-
Descrição: dc.descriptionFederal Institute of Triângulo Mineiro (IFTM), R. Belarmino Vilela Junqueira, S/N-
Descrição: dc.descriptionScience and Technology Institute Federal University of São Paulo (UNIFESP), Av. Cesare Mansueto Giulio Lattes, 1201-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), R. Cristóvão Colombo, 2265-
Descrição: dc.descriptionSchool of Dentistry University of Uberaba (UNIUBE), Av. Nenê Sabino, 1801-
Descrição: dc.descriptionDepartment of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia (UFU), Av. Amazonas, S/N-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), R. Cristóvão Colombo, 2265-
Descrição: dc.descriptionCAPES: 001-
Descrição: dc.descriptionCNPq: 304848/2018-2-
Descrição: dc.descriptionCNPq: 313365/2018-0-
Descrição: dc.descriptionCNPq: 430965/2018-4-
Descrição: dc.descriptionFAPEMIG: APQ-00578-18-
Descrição: dc.descriptionFAPEMIG: APQ-01129-21-
Idioma: dc.languageen-
Relação: dc.relationExpert Systems with Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectConvolutional neural network-
Palavras-chave: dc.subjectDysplasia-
Palavras-chave: dc.subjectHistological image-
Palavras-chave: dc.subjectOral cavity-
Palavras-chave: dc.subjectPolynomial classifier-
Título: dc.titleComputational analysis of histological images from hematoxylin and eosin-stained oral epithelial dysplasia tissue sections-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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