AI-based aortic stenosis classification in MRI scans
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/10071/30821 |
Resumo: | Aortic stenosis (AS) is a critical cardiovascular condition that necessitates precise diagnosis for effective patient care. Despite a limited dataset comprising only 202 images, our study employs transfer learning to investigate the efficacy of five convolutional neural network (CNN) models, coupled with advanced computer vision techniques, in accurately classifying AS. The VGG16 model stands out among the tested models, achieving 95% recall and F1-score. To fortify the model’s robustness and generalization, we implement various data augmentation techniques, including translation, rotation, flip, and brightness adjustment. These techniques aim to capture real-world image variations encountered in clinical settings. Validation, conducted using authentic data from Hospital Santa Maria, not only affirms the clinical applicability of our model but also highlights the potential to develop robust models with a limited number of images. The models undergo training after the images undergo a series of computer vision and data augmentation techniques, as detailed in this paper. These techniques augment the size of our dataset, contributing to improved model performance. In conclusion, our study illuminates the potential of AI-driven AS detection in MRI scans. The integration of transfer learning, CNN models, and data augmentation yields high accuracy rates, even with a small dataset, as validated in real clinical cases. |
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AI-based aortic stenosis classification in MRI scansMRI imagingAortic disease classificationAortic stenosisArtificial intelligenceDeep learningMRI classificationConvolutional neural networks (CNN)Transfer learningData augmentationAortic stenosis (AS) is a critical cardiovascular condition that necessitates precise diagnosis for effective patient care. Despite a limited dataset comprising only 202 images, our study employs transfer learning to investigate the efficacy of five convolutional neural network (CNN) models, coupled with advanced computer vision techniques, in accurately classifying AS. The VGG16 model stands out among the tested models, achieving 95% recall and F1-score. To fortify the model’s robustness and generalization, we implement various data augmentation techniques, including translation, rotation, flip, and brightness adjustment. These techniques aim to capture real-world image variations encountered in clinical settings. Validation, conducted using authentic data from Hospital Santa Maria, not only affirms the clinical applicability of our model but also highlights the potential to develop robust models with a limited number of images. The models undergo training after the images undergo a series of computer vision and data augmentation techniques, as detailed in this paper. These techniques augment the size of our dataset, contributing to improved model performance. In conclusion, our study illuminates the potential of AI-driven AS detection in MRI scans. The integration of transfer learning, CNN models, and data augmentation yields high accuracy rates, even with a small dataset, as validated in real clinical cases.MDPI2024-02-05T10:15:39Z2023-01-01T00:00:00Z20232024-02-05T10:13:24Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/30821eng2079-929210.3390/electronics12234835Elvas, L. B.Águas, P.Ferreira, J.Oliveira, J.Dias, J.Rosário, L. B.info: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:RCAAP2024-02-11T01:19:15Zoai:repositorio.iscte-iul.pt:10071/30821Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:37:36.451257Repositório 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 |
AI-based aortic stenosis classification in MRI scans |
title |
AI-based aortic stenosis classification in MRI scans |
spellingShingle |
AI-based aortic stenosis classification in MRI scans Elvas, L. B. MRI imaging Aortic disease classification Aortic stenosis Artificial intelligence Deep learning MRI classification Convolutional neural networks (CNN) Transfer learning Data augmentation |
title_short |
AI-based aortic stenosis classification in MRI scans |
title_full |
AI-based aortic stenosis classification in MRI scans |
title_fullStr |
AI-based aortic stenosis classification in MRI scans |
title_full_unstemmed |
AI-based aortic stenosis classification in MRI scans |
title_sort |
AI-based aortic stenosis classification in MRI scans |
author |
Elvas, L. B. |
author_facet |
Elvas, L. B. Águas, P. Ferreira, J. Oliveira, J. Dias, J. Rosário, L. B. |
author_role |
author |
author2 |
Águas, P. Ferreira, J. Oliveira, J. Dias, J. Rosário, L. B. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Elvas, L. B. Águas, P. Ferreira, J. Oliveira, J. Dias, J. Rosário, L. B. |
dc.subject.por.fl_str_mv |
MRI imaging Aortic disease classification Aortic stenosis Artificial intelligence Deep learning MRI classification Convolutional neural networks (CNN) Transfer learning Data augmentation |
topic |
MRI imaging Aortic disease classification Aortic stenosis Artificial intelligence Deep learning MRI classification Convolutional neural networks (CNN) Transfer learning Data augmentation |
description |
Aortic stenosis (AS) is a critical cardiovascular condition that necessitates precise diagnosis for effective patient care. Despite a limited dataset comprising only 202 images, our study employs transfer learning to investigate the efficacy of five convolutional neural network (CNN) models, coupled with advanced computer vision techniques, in accurately classifying AS. The VGG16 model stands out among the tested models, achieving 95% recall and F1-score. To fortify the model’s robustness and generalization, we implement various data augmentation techniques, including translation, rotation, flip, and brightness adjustment. These techniques aim to capture real-world image variations encountered in clinical settings. Validation, conducted using authentic data from Hospital Santa Maria, not only affirms the clinical applicability of our model but also highlights the potential to develop robust models with a limited number of images. The models undergo training after the images undergo a series of computer vision and data augmentation techniques, as detailed in this paper. These techniques augment the size of our dataset, contributing to improved model performance. In conclusion, our study illuminates the potential of AI-driven AS detection in MRI scans. The integration of transfer learning, CNN models, and data augmentation yields high accuracy rates, even with a small dataset, as validated in real clinical cases. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-01-01T00:00:00Z 2023 2024-02-05T10:15:39Z 2024-02-05T10:13:24Z |
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/10071/30821 |
url |
http://hdl.handle.net/10071/30821 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2079-9292 10.3390/electronics12234835 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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) |
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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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1799137426811125760 |