Mammographic CAD Systems Evaluation
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.46885/roentgen.v2i2.58 |
Resumo: | Computer‑Aided Systems can improve the performance of breast cancer diagnosis, helping the differentiation and classification of breast benign and malignant lesions. Breast lesions are strongly correlated with their shape, therefore, in this work, firstly, were used quantitative measures based on fractal dimension, which can help characterizing the smoothness or the roughness of mammographic lesion shape, being calculated through the box‑counting method, directly from manually segmented lesions, and after applying a region growing/erosion algorithm. Then, Convolutional neural networks (CNN), which recently have been successfully used in the medical field to detect and classify pathologies in different imaging modalities, were applied in mammographic images. Since the mammographic databases have a restricted number of samples, one way to solve this problem is using a transfer learning approach, in which a CNN, previously pre-trained with a large amount of labelled non-medical data, is subsequently finetuned using a smaller dataset of mammographic images. In this study were applied three different pre-trained networks and were made an evaluation if their performance to classify lesions in mammograms is affected by the use, or not, of normalized images. Was also evaluated the performance of a support vector machine fed with features extracted from the CNN and the combined use of handcrafted features to complement the CNN-extracted features. The obtained results are encouraging and reveal that both use of fractal dimension and CNN can help to improve computer-aided diagnostic of mammographic lesions, with AUC values around 81.3%. |
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oai:roentgen.pt:article/58 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Mammographic CAD Systems EvaluationAvaliação da Caracterização de Lesões em Mamografia com Recurso a Sistemas CADMammographyBreast CancerComputer-Aided Diagnosis (CAD)Fractal DimensionConvolutional Neural Network (CNN)Transfer LearningSupport Vector Machine (SVM)Deep LearningMamografiaCancro de mamaDiagnóstico Assistido por Computador (CAD)Dimensão FractalRedes Neuronais de Convolução (CNN)Transfer LearningDeep LearningSupport Vector Machine (SVM)Computer‑Aided Systems can improve the performance of breast cancer diagnosis, helping the differentiation and classification of breast benign and malignant lesions. Breast lesions are strongly correlated with their shape, therefore, in this work, firstly, were used quantitative measures based on fractal dimension, which can help characterizing the smoothness or the roughness of mammographic lesion shape, being calculated through the box‑counting method, directly from manually segmented lesions, and after applying a region growing/erosion algorithm. Then, Convolutional neural networks (CNN), which recently have been successfully used in the medical field to detect and classify pathologies in different imaging modalities, were applied in mammographic images. Since the mammographic databases have a restricted number of samples, one way to solve this problem is using a transfer learning approach, in which a CNN, previously pre-trained with a large amount of labelled non-medical data, is subsequently finetuned using a smaller dataset of mammographic images. In this study were applied three different pre-trained networks and were made an evaluation if their performance to classify lesions in mammograms is affected by the use, or not, of normalized images. Was also evaluated the performance of a support vector machine fed with features extracted from the CNN and the combined use of handcrafted features to complement the CNN-extracted features. The obtained results are encouraging and reveal that both use of fractal dimension and CNN can help to improve computer-aided diagnostic of mammographic lesions, with AUC values around 81.3%.Os sistemas CAD auxiliam a deteção e diferenciação de lesões benignas e malignas, aumentando a performance no diagnóstico do cancro da mama. Uma vez que as lesões da mama estão fortemente correlacionadas com a forma e contorno, neste estudo, foram aplicados dois métodos diferentes para classificação de lesões em imagens de mamografia. O primeiro consiste em medidas quantitativas baseadas na dimensão fractal, calculadas através da aplicação do método “box-counting”, diretamente em imagens de lesões segmentadas antes e após a aplicação de um algoritmo de dilatação/erosão. O segundo método baseou-se na aplicação de Redes Neuronais de Convolução (CNN), as quais têm demonstrado um elevado grau de sucesso, na deteção e classificação de patologias em diferentes modalidades de imagem médica, incluindo a mamografia. De forma a ultrapassar a limitação do reduzido número de amostras disponíveis nas bases de dados de mamografia, foi aplicado o método de “transfer learning”, no qual três modelos CNN pré-treinados num grande conjunto de dados foram ajustados de forma a permitir a classificação de lesões em imagens de mamografia antes e após a aplicação de um processo de normalização. Também foi avaliada a performance de uma SVM com a utilização de características extraídas das CNN isoladamente ou combinadas com “handcrafted features”. Os resultados obtidos são encorajadores e demonstram que tanto o uso da medida de dimensão fractal como das CNN pode ajudar a melhorar o diagnóstico automático de lesões em mamografia, o que se traduziu em valores de AUC até 81,3%.NUCLIRAD - Núcleo de Desenvolvimento dos Técnicos de Radiologia2021-07-19info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.46885/roentgen.v2i2.58https://doi.org/10.46885/roentgen.v2i2.58ROENTGEN-Scientific Journal of Radiological Techniques; Vol. 2 No. 2 (2021): One Profession, different paths; 41-49ROENTGEN-Revista Científica das Técnicas Radiológicas; v. 2 n. 2 (2021): Uma Profissão, diferentes caminhos; 41-492184-7657reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPporhttps://roentgen.pt/index.php/Principal/article/view/58https://roentgen.pt/index.php/Principal/article/view/58/44Direitos de Autor (c) 2021 ROENTGEN-Revista Científica das Técnicas Radiológicasinfo:eu-repo/semantics/openAccessPerre, Ana C.Freire, Luís C.2023-12-20T16:17:08Zoai:roentgen.pt:article/58Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:55:19.895782Repositório 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 |
Mammographic CAD Systems Evaluation Avaliação da Caracterização de Lesões em Mamografia com Recurso a Sistemas CAD |
title |
Mammographic CAD Systems Evaluation |
spellingShingle |
Mammographic CAD Systems Evaluation Perre, Ana C. Mammography Breast Cancer Computer-Aided Diagnosis (CAD) Fractal Dimension Convolutional Neural Network (CNN) Transfer Learning Support Vector Machine (SVM) Deep Learning Mamografia Cancro de mama Diagnóstico Assistido por Computador (CAD) Dimensão Fractal Redes Neuronais de Convolução (CNN) Transfer Learning Deep Learning Support Vector Machine (SVM) |
title_short |
Mammographic CAD Systems Evaluation |
title_full |
Mammographic CAD Systems Evaluation |
title_fullStr |
Mammographic CAD Systems Evaluation |
title_full_unstemmed |
Mammographic CAD Systems Evaluation |
title_sort |
Mammographic CAD Systems Evaluation |
author |
Perre, Ana C. |
author_facet |
Perre, Ana C. Freire, Luís C. |
author_role |
author |
author2 |
Freire, Luís C. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Perre, Ana C. Freire, Luís C. |
dc.subject.por.fl_str_mv |
Mammography Breast Cancer Computer-Aided Diagnosis (CAD) Fractal Dimension Convolutional Neural Network (CNN) Transfer Learning Support Vector Machine (SVM) Deep Learning Mamografia Cancro de mama Diagnóstico Assistido por Computador (CAD) Dimensão Fractal Redes Neuronais de Convolução (CNN) Transfer Learning Deep Learning Support Vector Machine (SVM) |
topic |
Mammography Breast Cancer Computer-Aided Diagnosis (CAD) Fractal Dimension Convolutional Neural Network (CNN) Transfer Learning Support Vector Machine (SVM) Deep Learning Mamografia Cancro de mama Diagnóstico Assistido por Computador (CAD) Dimensão Fractal Redes Neuronais de Convolução (CNN) Transfer Learning Deep Learning Support Vector Machine (SVM) |
description |
Computer‑Aided Systems can improve the performance of breast cancer diagnosis, helping the differentiation and classification of breast benign and malignant lesions. Breast lesions are strongly correlated with their shape, therefore, in this work, firstly, were used quantitative measures based on fractal dimension, which can help characterizing the smoothness or the roughness of mammographic lesion shape, being calculated through the box‑counting method, directly from manually segmented lesions, and after applying a region growing/erosion algorithm. Then, Convolutional neural networks (CNN), which recently have been successfully used in the medical field to detect and classify pathologies in different imaging modalities, were applied in mammographic images. Since the mammographic databases have a restricted number of samples, one way to solve this problem is using a transfer learning approach, in which a CNN, previously pre-trained with a large amount of labelled non-medical data, is subsequently finetuned using a smaller dataset of mammographic images. In this study were applied three different pre-trained networks and were made an evaluation if their performance to classify lesions in mammograms is affected by the use, or not, of normalized images. Was also evaluated the performance of a support vector machine fed with features extracted from the CNN and the combined use of handcrafted features to complement the CNN-extracted features. The obtained results are encouraging and reveal that both use of fractal dimension and CNN can help to improve computer-aided diagnostic of mammographic lesions, with AUC values around 81.3%. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-19 |
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://doi.org/10.46885/roentgen.v2i2.58 https://doi.org/10.46885/roentgen.v2i2.58 |
url |
https://doi.org/10.46885/roentgen.v2i2.58 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://roentgen.pt/index.php/Principal/article/view/58 https://roentgen.pt/index.php/Principal/article/view/58/44 |
dc.rights.driver.fl_str_mv |
Direitos de Autor (c) 2021 ROENTGEN-Revista Científica das Técnicas Radiológicas info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Direitos de Autor (c) 2021 ROENTGEN-Revista Científica das Técnicas Radiológicas |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
NUCLIRAD - Núcleo de Desenvolvimento dos Técnicos de Radiologia |
publisher.none.fl_str_mv |
NUCLIRAD - Núcleo de Desenvolvimento dos Técnicos de Radiologia |
dc.source.none.fl_str_mv |
ROENTGEN-Scientific Journal of Radiological Techniques; Vol. 2 No. 2 (2021): One Profession, different paths; 41-49 ROENTGEN-Revista Científica das Técnicas Radiológicas; v. 2 n. 2 (2021): Uma Profissão, diferentes caminhos; 41-49 2184-7657 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 |
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1799136439946969088 |