Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorFC-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorRoberto, Guilherme Freire-
Autor(es): dc.creatorLumini, Alessandra-
Autor(es): dc.creatorNeves, Leandro Alves [UNESP]-
Autor(es): dc.creatordo Nascimento, Marcelo Zanchetta-
Data de aceite: dc.date.accessioned2022-02-22T00:47:42Z-
Data de disponibilização: dc.date.available2022-02-22T00:47:42Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-03-15-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2020.114103-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/206637-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/206637-
Descrição: dc.descriptionClassification of histology images is a task that has been widely explored on recent computer vision researches. The most studied approach for this task has been the application of deep learning through a convolutional neural network (CNN) model. However, the use of CNNs in the context of histological images classification has yet some limitations such as the need of large datasets, the slow training time and the difficult to implement a generalized model able to classify different types of histology tissues. In this paper, we propose an ensemble model based on handcrafted fractal features and deep learning that consists of combining the classification of two CNNs by applying the sum rule. We apply feature extraction to obtain 300 fractal features from different histological datasets. These features are reshaped into a 10×10×3 matrix to compose an artificial image that is given as input to the first CNN. The second CNN model receives as input the correspondent original image. After combining the results of both CNNs, accuracies that range from 89.66% up to 99.62% were obtained from five different datasets. Moreover, our model was able to classify images from datasets with imbalanced classes, without the need for images having the same resolution, and in relative fast training time. We also verified that the obtained results are compatible with the most recent and relevant studies recently published in the context of histology image classification.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
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 38400-902 Uberlândia MG-
Descrição: dc.descriptionDepartment of Computer Science and Engineering (DISI) - University of Bologna Via dell'Università 50 47521 Cesena FC-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP) R. Cristóvão Colombo 2265 15054-000 São José do Rio Preto SP-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP) R. Cristóvão Colombo 2265 15054-000 São José do Rio Preto SP-
Descrição: dc.descriptionCNPq: #304848/2018-2-
Descrição: dc.descriptionCNPq: #313365/2018-0-
Descrição: dc.descriptionCNPq: #430965/2018-4-
Descrição: dc.descriptionCAPES: #88882.429128/2019-01-
Descrição: dc.descriptionFAPEMIG: #APQ-00578-18-
Idioma: dc.languageen-
Relação: dc.relationExpert Systems with Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClassification ensemble-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectFractal features-
Palavras-chave: dc.subjectHistology images-
Título: dc.titleFractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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