The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset

Detalhes bibliográficos
Autor(a) principal: Kandel, Ibrahem
Data de Publicação: 2020
Outros Autores: Castelli, Mauro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/98756
Resumo: Kandel, I., & Castelli, M. (2020). The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express, 6(4), 312-315. https://doi.org/10.1016/j.icte.2020.04.010
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spelling The effect of batch size on the generalizability of the convolutional neural networks on a histopathology datasetBatch sizeConvolutional neural networksDeep learningImage classificationMedical imagesSoftwareInformation SystemsHardware and ArchitectureComputer Networks and CommunicationsArtificial IntelligenceKandel, I., & Castelli, M. (2020). The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express, 6(4), 312-315. https://doi.org/10.1016/j.icte.2020.04.010Many hyperparameters have to be tuned to have a robust convolutional neural network that will be able to accurately classify images. One of the most important hyperparameters is the batch size, which is the number of images used to train a single forward and backward pass. In this study, the effect of batch size on the performance of convolutional neural networks and the impact of learning rates will be studied for image classification, specifically for medical images. To train the network faster, a VGG16 network with ImageNet weights was used in this experiment. Our results concluded that a higher batch size does not usually achieve high accuracy, and the learning rate and the optimizer used will have a significant impact as well. Lowering the learning rate and decreasing the batch size will allow the network to train better, especially in the case of fine-tuning.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNKandel, IbrahemCastelli, Mauro2020-06-03T00:55:41Z2020-122020-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article4application/pdfhttp://hdl.handle.net/10362/98756eng2191-1991PURE: 18415500https://doi.org/10.1016/j.icte.2020.04.010info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:45:58Zoai:run.unl.pt:10362/98756Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:03.033292Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset
title The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset
spellingShingle The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset
Kandel, Ibrahem
Batch size
Convolutional neural networks
Deep learning
Image classification
Medical images
Software
Information Systems
Hardware and Architecture
Computer Networks and Communications
Artificial Intelligence
title_short The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset
title_full The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset
title_fullStr The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset
title_full_unstemmed The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset
title_sort The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset
author Kandel, Ibrahem
author_facet Kandel, Ibrahem
Castelli, Mauro
author_role author
author2 Castelli, Mauro
author2_role author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Kandel, Ibrahem
Castelli, Mauro
dc.subject.por.fl_str_mv Batch size
Convolutional neural networks
Deep learning
Image classification
Medical images
Software
Information Systems
Hardware and Architecture
Computer Networks and Communications
Artificial Intelligence
topic Batch size
Convolutional neural networks
Deep learning
Image classification
Medical images
Software
Information Systems
Hardware and Architecture
Computer Networks and Communications
Artificial Intelligence
description Kandel, I., & Castelli, M. (2020). The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express, 6(4), 312-315. https://doi.org/10.1016/j.icte.2020.04.010
publishDate 2020
dc.date.none.fl_str_mv 2020-06-03T00:55:41Z
2020-12
2020-12-01T00:00:00Z
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://hdl.handle.net/10362/98756
url http://hdl.handle.net/10362/98756
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2191-1991
PURE: 18415500
https://doi.org/10.1016/j.icte.2020.04.010
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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application/pdf
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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