Assessment of the association of deep features with a polynomial algorithm for automated oral epithelial dysplasia grading

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dc.contributor.authorSilva, Adriano B.
dc.contributor.authorDe Oliveira, Cleber I. [UNESP]
dc.contributor.authorPereira, Danilo C.
dc.contributor.authorTosta, Thaina A. A.
dc.contributor.authorMartins, Alessandro S.
dc.contributor.authorLoyola, Adriano M.
dc.contributor.authorCardoso, Sergio V.
dc.contributor.authorDe Faria, Paulo R.
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.abstractOral epithelial dysplasia is a potentially malignant lesion that presents challenges for diagnosis. The use of digital systems in histological analysis can aid specialists to obtain data that allows a robust and fast grading process, but there are few methods in the literature proposing a grading system for this lesion. This study presents a method for oral epithelial dysplasia grading in histopathological images combining deep features and a polynomial classifier. The ResNet50 and AlexNet models were trained with the images and information was extracted from the convolutional layers, exploring convolutional neural networks via transfer learning. Then, the ReliefF algorithm was used to rank and select the most relevant features, which were given as an input to the polynomial classifier. The methodology was employed in a dataset with 296 regions of mice tongue images. The results were compared with the gold standard and other algorithms present in the literature. The classification stage presented AUC values ranging from 0.9663 to 0.9800. When compared to other algorithms present in the literature, our method provided relevant results regarding accuracy and AUC values. The proposed approach presented relevant results and can be used as a tool to aid pathologists in grading oral dysplastic lesions.en
dc.description.affiliationFederal University of Uberlândia (UFU) Faculty of Computer Science (FACOM)
dc.description.affiliationSão Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)
dc.description.affiliationFederal University of São Paulo (UNIFESP) Science and Technology Institute
dc.description.affiliationFederal Institute of Triângulo Mineiro (IFTM)
dc.description.affiliationFederal University of Uberlândia (UFU) Area of Oral Pathology School of Dentistry
dc.description.affiliationFederal University of Uberlândia (UFU) Institute of Biomedical Science Department of Histology and Morphology
dc.description.affiliationUnespSão Paulo State University (UNESP) Department of Computer Science and Statistics (DCCE)
dc.format.extent264-269
dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991758
dc.identifier.citationProceedings - 2022 35th Conference on Graphics, Patterns, and Images, SIBGRAPI 2022, p. 264-269.
dc.identifier.doi10.1109/SIBGRAPI55357.2022.9991758
dc.identifier.scopus2-s2.0-85146421187
dc.identifier.urihttp://hdl.handle.net/11449/248216
dc.language.isoeng
dc.relation.ispartofProceedings - 2022 35th Conference on Graphics, Patterns, and Images, SIBGRAPI 2022
dc.sourceScopus
dc.titleAssessment of the association of deep features with a polynomial algorithm for automated oral epithelial dysplasia gradingen
dc.typeTrabalho apresentado em evento

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