Breast tumor classification in ultrasound images using support vector machines and neural networks
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Publication Date: | 2016 |
Other Authors: | , , , , |
Format: | Article |
Language: | eng |
Source: | Research on Biomedical Engineering (Online) |
Download full: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402016000300283 |
Summary: | Abstract Introduction The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduced set of features performs better than a larger one. Methods This study evaluated the performance of support vector machines (SVM) with different kernels combinations, and neural networks with different stop criteria, for classifying breast cancer nodules. Twenty-two morphological features from the contour of 100 BUS images were used as input for classifiers and then a scalar feature selection technique with correlation was used to reduce the features dataset. Results The best results obtained for accuracy and area under ROC curve were 96.98% and 0.980, respectively, both with neural networks using the whole set of features. Conclusion The performance obtained with neural networks with the selected stop criterion was better than the ones obtained with SVM. Whilst using neural networks the results were better with all 22 features, SVM classifiers performed better with a reduced set of 6 features. |
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Breast tumor classification in ultrasound images using support vector machines and neural networksBreast tumorsBreast ultrasound imagesNeural networkSupport vector machineAbstract Introduction The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduced set of features performs better than a larger one. Methods This study evaluated the performance of support vector machines (SVM) with different kernels combinations, and neural networks with different stop criteria, for classifying breast cancer nodules. Twenty-two morphological features from the contour of 100 BUS images were used as input for classifiers and then a scalar feature selection technique with correlation was used to reduce the features dataset. Results The best results obtained for accuracy and area under ROC curve were 96.98% and 0.980, respectively, both with neural networks using the whole set of features. Conclusion The performance obtained with neural networks with the selected stop criterion was better than the ones obtained with SVM. Whilst using neural networks the results were better with all 22 features, SVM classifiers performed better with a reduced set of 6 features.Sociedade Brasileira de Engenharia Biomédica2016-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402016000300283Research on Biomedical Engineering v.32 n.3 2016reponame:Research on Biomedical Engineering (Online)instname:Sociedade Brasileira de Engenharia Biomédica (SBEB)instacron:SBEB10.1590/2446-4740.04915info:eu-repo/semantics/openAccessNascimento,Carmina Dessana LimaSilva,Sérgio Deodoro de SouzaSilva,Thales Araújo daPereira,Wagner Coelho de AlbuquerqueCosta,Marly Guimarães FernandesCosta Filho,Cicero Ferreira Fernandeseng2016-10-24T00:00:00Zoai:scielo:S2446-47402016000300283Revistahttp://www.rbejournal.org/https://old.scielo.br/oai/scielo-oai.php||rbe@rbejournal.org2446-47402446-4732opendoar:2016-10-24T00:00Research on Biomedical Engineering (Online) - Sociedade Brasileira de Engenharia Biomédica (SBEB)false |
dc.title.none.fl_str_mv |
Breast tumor classification in ultrasound images using support vector machines and neural networks |
title |
Breast tumor classification in ultrasound images using support vector machines and neural networks |
spellingShingle |
Breast tumor classification in ultrasound images using support vector machines and neural networks Nascimento,Carmina Dessana Lima Breast tumors Breast ultrasound images Neural network Support vector machine |
title_short |
Breast tumor classification in ultrasound images using support vector machines and neural networks |
title_full |
Breast tumor classification in ultrasound images using support vector machines and neural networks |
title_fullStr |
Breast tumor classification in ultrasound images using support vector machines and neural networks |
title_full_unstemmed |
Breast tumor classification in ultrasound images using support vector machines and neural networks |
title_sort |
Breast tumor classification in ultrasound images using support vector machines and neural networks |
author |
Nascimento,Carmina Dessana Lima |
author_facet |
Nascimento,Carmina Dessana Lima Silva,Sérgio Deodoro de Souza Silva,Thales Araújo da Pereira,Wagner Coelho de Albuquerque Costa,Marly Guimarães Fernandes Costa Filho,Cicero Ferreira Fernandes |
author_role |
author |
author2 |
Silva,Sérgio Deodoro de Souza Silva,Thales Araújo da Pereira,Wagner Coelho de Albuquerque Costa,Marly Guimarães Fernandes Costa Filho,Cicero Ferreira Fernandes |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Nascimento,Carmina Dessana Lima Silva,Sérgio Deodoro de Souza Silva,Thales Araújo da Pereira,Wagner Coelho de Albuquerque Costa,Marly Guimarães Fernandes Costa Filho,Cicero Ferreira Fernandes |
dc.subject.por.fl_str_mv |
Breast tumors Breast ultrasound images Neural network Support vector machine |
topic |
Breast tumors Breast ultrasound images Neural network Support vector machine |
description |
Abstract Introduction The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduced set of features performs better than a larger one. Methods This study evaluated the performance of support vector machines (SVM) with different kernels combinations, and neural networks with different stop criteria, for classifying breast cancer nodules. Twenty-two morphological features from the contour of 100 BUS images were used as input for classifiers and then a scalar feature selection technique with correlation was used to reduce the features dataset. Results The best results obtained for accuracy and area under ROC curve were 96.98% and 0.980, respectively, both with neural networks using the whole set of features. Conclusion The performance obtained with neural networks with the selected stop criterion was better than the ones obtained with SVM. Whilst using neural networks the results were better with all 22 features, SVM classifiers performed better with a reduced set of 6 features. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-09-01 |
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=S2446-47402016000300283 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402016000300283 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2446-4740.04915 |
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.32 n.3 2016 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 |
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1752126288634576896 |