Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

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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.creatorTenguam, Jaqueline J.-
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.accessioned2025-08-21T21:33:00Z-
Data de disponibilização: dc.date.available2025-08-21T21:33:00Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/app14031084-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/301306-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/301306-
Descrição: dc.descriptionIn this paper, we propose an approach based on ensemble learning to classify histology tissues stained with hematoxylin and eosin. The proposal was applied to representative images of colorectal cancer, oral epithelial dysplasia, non-Hodgkin’s lymphoma, and liver tissues (the classification of gender and age from liver tissue samples). The ensemble learning considered multiple combinations of techniques that are commonly used to develop computer-aided diagnosis methods in medical imaging. The feature extraction was defined with different descriptors, exploring the deep learning and handcrafted methods. The deep-learned features were obtained using five different convolutional neural network architectures. The handcrafted features were representatives of fractal techniques (multidimensional and multiscale approaches), Haralick descriptors, and local binary patterns. A two-stage feature selection process (ranking with metaheuristics) was defined to obtain the main combinations of descriptors and, consequently, techniques. Each combination was tested through a rigorous ensemble process, exploring heterogeneous classifiers, such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. The ensemble learning presented here provided accuracy rates from 90.72% to 100.00% and offered relevant information about the combinations of techniques in multiple histological images and the main features present in the top-performing solutions, using smaller sets of descriptors (limited to a maximum of 53), which involved each ensemble process and solutions that have not yet been explored. The developed methodology, i.e., making the knowledge of each ensemble learning comprehensible to specialists, complements the main contributions of this study to supporting the development of computer-aided diagnosis systems for histological images.-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São Paulo-
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, São Paulo-
Descrição: dc.descriptionDepartment of Histology and Morphology Institute of Biomedical Science Federal University of Uberlândia (UFU), Av. Amazonas, S/N, Minas Gerais-
Descrição: dc.descriptionArea of Oral Pathology School of Dentistry Federal University of Uberlândia (UFU), R. Ceará—Umuarama, Minas Gerais-
Descrição: dc.descriptionFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU), Avenida João Naves de Ávila 2121, Bl.B, Minas Gerais-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, São Paulo-
Idioma: dc.languageen-
Relação: dc.relationApplied Sciences (Switzerland)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectdeep-learned features-
Palavras-chave: dc.subjectensemble learning-
Palavras-chave: dc.subjecthandcrafted features-
Palavras-chave: dc.subjecthistological images-
Palavras-chave: dc.subjecttwo-stage feature selection method-
Título: dc.titleEnsemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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