An innovative Faster R-CNN-Based framework for breast cancer detection in MRI

Detalhes bibliográficos
Autor(a) principal: Raimundo, João Nuno Centeno
Data de Publicação: 2023
Outros Autores: Fontes, João Pedro Pereira, Magalhães, Luís Gonzaga Mendes, Guevara Lopez, Miguel Angel
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/87266
Resumo: Replacing lung cancer as the most commonly diagnosed cancer globally, breast cancer (BC) today accounts for 1 in 8 cancer diagnoses and a total of 2.3 million new cases in both sexes combined. An estimated 685,000 women died from BC in 2020, corresponding to 16% or 1 in every 6 cancer deaths in women. BC represents a quarter of a total of cancer cases in females and by far the most commonly diagnosed cancer in women in 2020. However, when detected in the early stages of the disease, treatment methods have proven to be very effective in increasing life expectancy and, in many cases, patients fully recover. Several medical imaging modalities, such as X-rays Mammography (MG), Ultrasound (US), Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and Digital Tomosynthesis (DT) have been explored to support radiologists/physicians in clinical decision-making workflows for the detection and diagnosis of BC. In this work, we propose a novel Faster R-CNN-based framework to automate the detection of BC pathological Lesions in MRI. As a main contribution, we have developed and experimentally (statistically) validated an innovative method improving the “breast MRI preprocessing phase” to select the patient’s slices (images) and associated bounding boxes representing pathological lesions. In this way, it is possible to create a more robust training (benchmarking) dataset to feed Deep Learning (DL) models, reducing the computation time and the dimension of the dataset, and more importantly, to identify with high accuracy the specific regions (bounding boxes) for each of the patient’s images, in which a possible pathological lesion (tumor) has been identified. As a result, in an experimental setting using a fully annotated dataset (released to the public domain) comprising a total of 922 MRI-based BC patient cases, we have achieved, as the most accurate trained model, an accuracy rate of 97.83%, and subsequently, applying a ten-fold cross-validation method, a mean accuracy on the trained models of 94.46% and an associated standard deviation of 2.43%.
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spelling An innovative Faster R-CNN-Based framework for breast cancer detection in MRIBreast cancer detectionMagnetic resonance imagingComputer visionMachine learningDeep learningConvolutional neural networksReplacing lung cancer as the most commonly diagnosed cancer globally, breast cancer (BC) today accounts for 1 in 8 cancer diagnoses and a total of 2.3 million new cases in both sexes combined. An estimated 685,000 women died from BC in 2020, corresponding to 16% or 1 in every 6 cancer deaths in women. BC represents a quarter of a total of cancer cases in females and by far the most commonly diagnosed cancer in women in 2020. However, when detected in the early stages of the disease, treatment methods have proven to be very effective in increasing life expectancy and, in many cases, patients fully recover. Several medical imaging modalities, such as X-rays Mammography (MG), Ultrasound (US), Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and Digital Tomosynthesis (DT) have been explored to support radiologists/physicians in clinical decision-making workflows for the detection and diagnosis of BC. In this work, we propose a novel Faster R-CNN-based framework to automate the detection of BC pathological Lesions in MRI. As a main contribution, we have developed and experimentally (statistically) validated an innovative method improving the “breast MRI preprocessing phase” to select the patient’s slices (images) and associated bounding boxes representing pathological lesions. In this way, it is possible to create a more robust training (benchmarking) dataset to feed Deep Learning (DL) models, reducing the computation time and the dimension of the dataset, and more importantly, to identify with high accuracy the specific regions (bounding boxes) for each of the patient’s images, in which a possible pathological lesion (tumor) has been identified. As a result, in an experimental setting using a fully annotated dataset (released to the public domain) comprising a total of 922 MRI-based BC patient cases, we have achieved, as the most accurate trained model, an accuracy rate of 97.83%, and subsequently, applying a ten-fold cross-validation method, a mean accuracy on the trained models of 94.46% and an associated standard deviation of 2.43%.This paper is financed by Instituto Politécnico de Setúbal, PortugalMultidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoRaimundo, João Nuno CentenoFontes, João Pedro PereiraMagalhães, Luís Gonzaga MendesGuevara Lopez, Miguel Angel2023-08-232023-08-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/87266engRaimundo, J.N.C.; Fontes, J.P.P.; Gonzaga Mendes Magalhães, L.; Guevara Lopez, M.A. An Innovative Faster R-CNN-Based Framework for Breast Cancer Detection in MRI. J. Imaging 2023, 9, 169. https://doi.org/10.3390/jimaging90901692313-433X10.3390/jimaging9090169169https://www.mdpi.com/2313-433X/9/9/169info: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:RCAAP2023-11-18T01:17:01Zoai:repositorium.sdum.uminho.pt:1822/87266Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:53:57.859345Repositório 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 An innovative Faster R-CNN-Based framework for breast cancer detection in MRI
title An innovative Faster R-CNN-Based framework for breast cancer detection in MRI
spellingShingle An innovative Faster R-CNN-Based framework for breast cancer detection in MRI
Raimundo, João Nuno Centeno
Breast cancer detection
Magnetic resonance imaging
Computer vision
Machine learning
Deep learning
Convolutional neural networks
title_short An innovative Faster R-CNN-Based framework for breast cancer detection in MRI
title_full An innovative Faster R-CNN-Based framework for breast cancer detection in MRI
title_fullStr An innovative Faster R-CNN-Based framework for breast cancer detection in MRI
title_full_unstemmed An innovative Faster R-CNN-Based framework for breast cancer detection in MRI
title_sort An innovative Faster R-CNN-Based framework for breast cancer detection in MRI
author Raimundo, João Nuno Centeno
author_facet Raimundo, João Nuno Centeno
Fontes, João Pedro Pereira
Magalhães, Luís Gonzaga Mendes
Guevara Lopez, Miguel Angel
author_role author
author2 Fontes, João Pedro Pereira
Magalhães, Luís Gonzaga Mendes
Guevara Lopez, Miguel Angel
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Raimundo, João Nuno Centeno
Fontes, João Pedro Pereira
Magalhães, Luís Gonzaga Mendes
Guevara Lopez, Miguel Angel
dc.subject.por.fl_str_mv Breast cancer detection
Magnetic resonance imaging
Computer vision
Machine learning
Deep learning
Convolutional neural networks
topic Breast cancer detection
Magnetic resonance imaging
Computer vision
Machine learning
Deep learning
Convolutional neural networks
description Replacing lung cancer as the most commonly diagnosed cancer globally, breast cancer (BC) today accounts for 1 in 8 cancer diagnoses and a total of 2.3 million new cases in both sexes combined. An estimated 685,000 women died from BC in 2020, corresponding to 16% or 1 in every 6 cancer deaths in women. BC represents a quarter of a total of cancer cases in females and by far the most commonly diagnosed cancer in women in 2020. However, when detected in the early stages of the disease, treatment methods have proven to be very effective in increasing life expectancy and, in many cases, patients fully recover. Several medical imaging modalities, such as X-rays Mammography (MG), Ultrasound (US), Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and Digital Tomosynthesis (DT) have been explored to support radiologists/physicians in clinical decision-making workflows for the detection and diagnosis of BC. In this work, we propose a novel Faster R-CNN-based framework to automate the detection of BC pathological Lesions in MRI. As a main contribution, we have developed and experimentally (statistically) validated an innovative method improving the “breast MRI preprocessing phase” to select the patient’s slices (images) and associated bounding boxes representing pathological lesions. In this way, it is possible to create a more robust training (benchmarking) dataset to feed Deep Learning (DL) models, reducing the computation time and the dimension of the dataset, and more importantly, to identify with high accuracy the specific regions (bounding boxes) for each of the patient’s images, in which a possible pathological lesion (tumor) has been identified. As a result, in an experimental setting using a fully annotated dataset (released to the public domain) comprising a total of 922 MRI-based BC patient cases, we have achieved, as the most accurate trained model, an accuracy rate of 97.83%, and subsequently, applying a ten-fold cross-validation method, a mean accuracy on the trained models of 94.46% and an associated standard deviation of 2.43%.
publishDate 2023
dc.date.none.fl_str_mv 2023-08-23
2023-08-23T00:00:00Z
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 https://hdl.handle.net/1822/87266
url https://hdl.handle.net/1822/87266
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Raimundo, J.N.C.; Fontes, J.P.P.; Gonzaga Mendes Magalhães, L.; Guevara Lopez, M.A. An Innovative Faster R-CNN-Based Framework for Breast Cancer Detection in MRI. J. Imaging 2023, 9, 169. https://doi.org/10.3390/jimaging9090169
2313-433X
10.3390/jimaging9090169
169
https://www.mdpi.com/2313-433X/9/9/169
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 Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (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
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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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