Breast cancer diagnosis based on mammary thermography and extreme learning machines
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research on Biomedical Engineering (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000100045 |
Resumo: | Abstract Introduction Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness. |
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Breast cancer diagnosis based on mammary thermography and extreme learning machinesBreast cancer early diagnosisThermographic imagesMammary thermographyArtificial neural networksExtreme learning machinesAbstract Introduction Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.Sociedade Brasileira de Engenharia Biomédica2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000100045Research on Biomedical Engineering v.34 n.1 2018reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.05217info:eu-repo/semantics/openAccessSantana,Maíra Araújo dePereira,Jessiane Mônica SilvaSilva,Fabrício Lucimar daLima,Nigel Mendes deSousa,Felipe Nunes deArruda,Guilherme Max Silva deLima,Rita de Cássia Fernandes deSilva,Washington Wagner Azevedo daSantos,Wellington Pinheiro doseng2018-04-18T00:00:00Zoai:scielo:S2446-47402018000100045Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2018-04-18T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false |
dc.title.none.fl_str_mv |
Breast cancer diagnosis based on mammary thermography and extreme learning machines |
title |
Breast cancer diagnosis based on mammary thermography and extreme learning machines |
spellingShingle |
Breast cancer diagnosis based on mammary thermography and extreme learning machines Santana,Maíra Araújo de Breast cancer early diagnosis Thermographic images Mammary thermography Artificial neural networks Extreme learning machines |
title_short |
Breast cancer diagnosis based on mammary thermography and extreme learning machines |
title_full |
Breast cancer diagnosis based on mammary thermography and extreme learning machines |
title_fullStr |
Breast cancer diagnosis based on mammary thermography and extreme learning machines |
title_full_unstemmed |
Breast cancer diagnosis based on mammary thermography and extreme learning machines |
title_sort |
Breast cancer diagnosis based on mammary thermography and extreme learning machines |
author |
Santana,Maíra Araújo de |
author_facet |
Santana,Maíra Araújo de Pereira,Jessiane Mônica Silva Silva,Fabrício Lucimar da Lima,Nigel Mendes de Sousa,Felipe Nunes de Arruda,Guilherme Max Silva de Lima,Rita de Cássia Fernandes de Silva,Washington Wagner Azevedo da Santos,Wellington Pinheiro dos |
author_role |
author |
author2 |
Pereira,Jessiane Mônica Silva Silva,Fabrício Lucimar da Lima,Nigel Mendes de Sousa,Felipe Nunes de Arruda,Guilherme Max Silva de Lima,Rita de Cássia Fernandes de Silva,Washington Wagner Azevedo da Santos,Wellington Pinheiro dos |
author2_role |
author author author author author author author author |
dc.contributor.author.fl_str_mv |
Santana,Maíra Araújo de Pereira,Jessiane Mônica Silva Silva,Fabrício Lucimar da Lima,Nigel Mendes de Sousa,Felipe Nunes de Arruda,Guilherme Max Silva de Lima,Rita de Cássia Fernandes de Silva,Washington Wagner Azevedo da Santos,Wellington Pinheiro dos |
dc.subject.por.fl_str_mv |
Breast cancer early diagnosis Thermographic images Mammary thermography Artificial neural networks Extreme learning machines |
topic |
Breast cancer early diagnosis Thermographic images Mammary thermography Artificial neural networks Extreme learning machines |
description |
Abstract Introduction Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000100045 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402018000100045 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2446-4740.05217 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Engenharia Biomédica |
publisher.none.fl_str_mv |
Sociedade Brasileira de Engenharia Biomédica |
dc.source.none.fl_str_mv |
Research on Biomedical Engineering v.34 n.1 2018 reponame:Research on Biomedical Engineering (Online) instname:Sociedade Brasileira de Engenharia Biomédica (SBEB) instacron:SBEB |
instname_str |
Sociedade Brasileira de Engenharia Biomédica (SBEB) |
instacron_str |
SBEB |
institution |
SBEB |
reponame_str |
Research on Biomedical Engineering (Online) |
collection |
Research on Biomedical Engineering (Online) |
repository.name.fl_str_mv |
Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB) |
repository.mail.fl_str_mv |
||rbe@rbejournal.org |
_version_ |
1752126288780328960 |