Mammographic CAD Systems Evaluation

Detalhes bibliográficos
Autor(a) principal: Perre, Ana C.
Data de Publicação: 2021
Outros Autores: Freire, Luís C.
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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spelling 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
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