Classification of Multiple H&E Images via an Ensemble Computational Scheme

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Porto-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.creatorLongo, Leonardo H. da Costa-
Autor(es): dc.creatorRoberto, Guilherme F.-
Autor(es): dc.creatorTosta, Thaína A. A.-
Autor(es): dc.creatorde Faria, Paulo R.-
Autor(es): dc.creatorLoyola, Adriano M.-
Autor(es): dc.creatorCardoso, Sérgio V.-
Autor(es): dc.creatorSilva, Adriano B.-
Autor(es): dc.creatordo Nascimento, Marcelo Z.-
Autor(es): dc.creatorNeves, Leandro A.-
Data de aceite: dc.date.accessioned2025-08-21T22:04:48Z-
Data de disponibilização: dc.date.available2025-08-21T22:04:48Z-
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/10.3390/e26010034-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/304689-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/304689-
Descrição: dc.descriptionIn this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of (Formula presented.) to (Formula presented.), with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP-
Descrição: dc.descriptionDepartment of Informatics Engineering Faculty of Engineering University of Porto, Dr. Roberto Frias, sn-
Descrição: dc.descriptionScience and Technology Institute Federal University of São Paulo (UNIFESP), Avenida Cesare Mansueto Giulio Lattes, 1201, SP-
Descrição: dc.descriptionDepartment of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia (UFU), Av. Amazonas, S/N, MG-
Descrição: dc.descriptionArea of Oral Pathology School of Dentistry Federal University of Uberlândia (UFU), R. Ceará—Umuarama, MG-
Descrição: dc.descriptionFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, MG-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP-
Idioma: dc.languageen-
Relação: dc.relationEntropy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectclassification-
Palavras-chave: dc.subjectdeep-learned features-
Palavras-chave: dc.subjectensembles-
Palavras-chave: dc.subjectfractal techniques-
Palavras-chave: dc.subjectheterogeneous classifiers-
Palavras-chave: dc.subjecthistological images-
Palavras-chave: dc.subjectxAI representation-
Título: dc.titleClassification of Multiple H&E Images via an Ensemble Computational Scheme-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.