Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.eswa.2020.114103 http://hdl.handle.net/11449/206637 |
Resumo: | Classification 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. |
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Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology imagesClassification ensembleDeep learningFractal featuresHistology imagesClassification 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.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Faculty of Computer Science (FACOM) - Federal University of Uberlândia (UFU) Av. João Naves de Ávila 2121 BLB 38400-902 Uberlândia MGDepartment of Computer Science and Engineering (DISI) - University of Bologna Via dell'Università 50 47521 Cesena FCDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP) R. Cristóvão Colombo 2265 15054-000 São José do Rio Preto SPDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP) R. Cristóvão Colombo 2265 15054-000 São José do Rio Preto SPCNPq: #304848/2018-2CNPq: #313365/2018-0CNPq: #430965/2018-4CAPES: #88882.429128/2019-01FAPEMIG: #APQ-00578-18Universidade Federal de Uberlândia (UFU)FCUniversidade Estadual Paulista (Unesp)Roberto, Guilherme FreireLumini, AlessandraNeves, Leandro Alves [UNESP]do Nascimento, Marcelo Zanchetta2021-06-25T10:35:39Z2021-06-25T10:35:39Z2021-03-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.eswa.2020.114103Expert Systems with Applications, v. 166.0957-4174http://hdl.handle.net/11449/20663710.1016/j.eswa.2020.1141032-s2.0-85092357205Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengExpert Systems with Applicationsinfo:eu-repo/semantics/openAccess2021-10-23T08:24:56Zoai:repositorio.unesp.br:11449/206637Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:46:36.163943Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images |
title |
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images |
spellingShingle |
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images Roberto, Guilherme Freire Classification ensemble Deep learning Fractal features Histology images |
title_short |
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images |
title_full |
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images |
title_fullStr |
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images |
title_full_unstemmed |
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images |
title_sort |
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images |
author |
Roberto, Guilherme Freire |
author_facet |
Roberto, Guilherme Freire Lumini, Alessandra Neves, Leandro Alves [UNESP] do Nascimento, Marcelo Zanchetta |
author_role |
author |
author2 |
Lumini, Alessandra Neves, Leandro Alves [UNESP] do Nascimento, Marcelo Zanchetta |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Uberlândia (UFU) FC Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Roberto, Guilherme Freire Lumini, Alessandra Neves, Leandro Alves [UNESP] do Nascimento, Marcelo Zanchetta |
dc.subject.por.fl_str_mv |
Classification ensemble Deep learning Fractal features Histology images |
topic |
Classification ensemble Deep learning Fractal features Histology images |
description |
Classification 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T10:35:39Z 2021-06-25T10:35:39Z 2021-03-15 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.eswa.2020.114103 Expert Systems with Applications, v. 166. 0957-4174 http://hdl.handle.net/11449/206637 10.1016/j.eswa.2020.114103 2-s2.0-85092357205 |
url |
http://dx.doi.org/10.1016/j.eswa.2020.114103 http://hdl.handle.net/11449/206637 |
identifier_str_mv |
Expert Systems with Applications, v. 166. 0957-4174 10.1016/j.eswa.2020.114103 2-s2.0-85092357205 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Expert Systems with Applications |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129551224537088 |