Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images

Detalhes bibliográficos
Autor(a) principal: Kandel, Ibrahem
Data de Publicação: 2020
Outros Autores: Castelli, Mauro, Popovič, Aleš
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/105501
Resumo: Kandel, I., Castelli, M., & Popovič, A. (2020). Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images. Journal of Imaging, 6(9), 1-17. [0092]. https://doi.org/10.3390/JIMAGING6090092
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spelling Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology ImagesConvolutional neural networksDeep learningImage classificationMedical imagesOptimizersTransfer learningRadiology Nuclear Medicine and imagingComputer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignElectrical and Electronic EngineeringSDG 3 - Good Health and Well-beingKandel, I., Castelli, M., & Popovič, A. (2020). Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images. Journal of Imaging, 6(9), 1-17. [0092]. https://doi.org/10.3390/JIMAGING6090092The classification of histopathology images requires an experienced physician with years of experience to classify the histopathology images accurately. In this study, an algorithm was developed to assist physicians in classifying histopathology images; the algorithm receives the histopathology image as an input and produces the percentage of cancer presence. The primary classifier used in this algorithm is the convolutional neural network, which is a state-of-the-art classifier used in image classification as it can classify images without relying on the manual selection of features from each image. The main aim of this research is to improve the robustness of the classifier used by comparing six different first-order stochastic gradient-based optimizers to select the best for this particular dataset. The dataset used to train the classifier is the PatchCamelyon public dataset, which consists of 220,025 images to train the classifier; the dataset is composed of 60% positive images and 40% negative images, and 57,458 images to test its performance. The classifier was trained on 80% of the images and validated on the rest of 20% of the images; then, it was tested on the test set. The optimizers were evaluated based on their AUC of the ROC curve. The results show that the adaptative based optimizers achieved the highest results except for AdaGrad that achieved the lowest results.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNKandel, IbrahemCastelli, MauroPopovič, Aleš2020-10-12T22:52:20Z2020-09-082020-09-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/105501eng2313-433XPURE: 20289417https://doi.org/10.3390/JIMAGING6090092info: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:RCAAP2024-03-11T04:50:39Zoai:run.unl.pt:10362/105501Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:29.891453Repositório 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 Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
title Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
spellingShingle Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
Kandel, Ibrahem
Convolutional neural networks
Deep learning
Image classification
Medical images
Optimizers
Transfer learning
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
SDG 3 - Good Health and Well-being
title_short Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
title_full Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
title_fullStr Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
title_full_unstemmed Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
title_sort Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
author Kandel, Ibrahem
author_facet Kandel, Ibrahem
Castelli, Mauro
Popovič, Aleš
author_role author
author2 Castelli, Mauro
Popovič, Aleš
author2_role author
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
Popovič, Aleš
dc.subject.por.fl_str_mv Convolutional neural networks
Deep learning
Image classification
Medical images
Optimizers
Transfer learning
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
SDG 3 - Good Health and Well-being
topic Convolutional neural networks
Deep learning
Image classification
Medical images
Optimizers
Transfer learning
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering
SDG 3 - Good Health and Well-being
description Kandel, I., Castelli, M., & Popovič, A. (2020). Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images. Journal of Imaging, 6(9), 1-17. [0092]. https://doi.org/10.3390/JIMAGING6090092
publishDate 2020
dc.date.none.fl_str_mv 2020-10-12T22:52:20Z
2020-09-08
2020-09-08T00:00:00Z
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/10362/105501
url http://hdl.handle.net/10362/105501
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2313-433X
PURE: 20289417
https://doi.org/10.3390/JIMAGING6090092
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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