A novel architecture to classify histopathology images using convolutional neural networks
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/10362/98755 |
Resumo: | Kandel, I., & Castelli, M. (2020). A novel architecture to classify histopathology images using convolutional neural networks. Applied Sciences (Switzerland), 10(8), 1-17. [2929]. https://doi.org/10.3390/APP10082929 |
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A novel architecture to classify histopathology images using convolutional neural networksConvolutional neural networksDeep learningHistopathology imagesImage classificationMaterials Science(all)InstrumentationEngineering(all)Process Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer ProcessesKandel, I., & Castelli, M. (2020). A novel architecture to classify histopathology images using convolutional neural networks. Applied Sciences (Switzerland), 10(8), 1-17. [2929]. https://doi.org/10.3390/APP10082929Histopathology is the study of tissue structure under the microscope to determine if the cells are normal or abnormal. Histopathology is a very important exam that is used to determine the patients' treatment plan. The classification of histopathology images is very difficult to even an experienced pathologist, and a second opinion is often needed. Convolutional neural network (CNN), a particular type of deep learning architecture, obtained outstanding results in computer vision tasks like image classification. In this paper, we propose a novel CNN architecture to classify histopathology images. The proposed model consists of 15 convolution layers and two fully connected layers. A comparison between different activation functions was performed to detect the most efficient one, taking into account two different optimizers. To train and evaluate the proposed model, the publicly available PatchCamelyon dataset was used. The dataset consists of 220,000 annotated images for training and 57,000 unannotated images for testing. The proposed model achieved higher performance compared to the state-of-the-art architectures with an AUC of 95.46%.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNKandel, IbrahemCastelli, Mauro2020-06-03T00:55:39Z2020-04-232020-04-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article17application/pdfhttp://hdl.handle.net/10362/98755eng2076-3417PURE: 18414923https://doi.org/10.3390/APP10082929info: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:RCAAP2024-05-22T17:45:48Zoai:run.unl.pt:10362/98755Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:45:48Repositório 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 |
A novel architecture to classify histopathology images using convolutional neural networks |
title |
A novel architecture to classify histopathology images using convolutional neural networks |
spellingShingle |
A novel architecture to classify histopathology images using convolutional neural networks Kandel, Ibrahem Convolutional neural networks Deep learning Histopathology images Image classification Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
title_short |
A novel architecture to classify histopathology images using convolutional neural networks |
title_full |
A novel architecture to classify histopathology images using convolutional neural networks |
title_fullStr |
A novel architecture to classify histopathology images using convolutional neural networks |
title_full_unstemmed |
A novel architecture to classify histopathology images using convolutional neural networks |
title_sort |
A novel architecture to classify histopathology images using convolutional neural networks |
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 |
Convolutional neural networks Deep learning Histopathology images Image classification Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
topic |
Convolutional neural networks Deep learning Histopathology images Image classification Materials Science(all) Instrumentation Engineering(all) Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
description |
Kandel, I., & Castelli, M. (2020). A novel architecture to classify histopathology images using convolutional neural networks. Applied Sciences (Switzerland), 10(8), 1-17. [2929]. https://doi.org/10.3390/APP10082929 |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-06-03T00:55:39Z 2020-04-23 2020-04-23T00:00:00Z |
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/10362/98755 |
url |
http://hdl.handle.net/10362/98755 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 PURE: 18414923 https://doi.org/10.3390/APP10082929 |
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 |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817545744566976512 |