Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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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 |
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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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1799138019859496960 |