Real time mass classification for mammographic images: a Driven CADx scheme
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Health Review |
Texto Completo: | https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/61095 |
Resumo: | Computer-Aided Diagnosis (CADx) schemes have been proposed to serve as a supplementary image analysis tool in mammography. Experienced radiologists tend to be more assertive to such schemes in assisting their interpretation rather than solely relying on their ability to detect suspicious signals. This study focuses on a simplified version of a previously developed mammography CADx scheme, which was initially designed for digitized film, but is now specifically aimed at classifying breast nodules marked as regions of interest on digital images. This “driven” CADx scheme provides prompt indications regarding whether the selected nodule is deemed normal or suspicious. Its performance was evaluated through tests conducted on different mammograms sets – one with large number of images selected from DDSM database for training, testing and validation of classification parameters, and other comprising direct digital images from InBreast database. Remarkably, similar rates were observed for sensitivity, specificity and accuracy across these two sets (83%, 67% and 72%, respectively). The classification attributes were associated to contour, density and texture. Furthermore, a third test was conducted involving radiologists analyzing digital mammograms obtained from a specific full field digital mammography (FFDM) unit. Results showed that the Driven CADx scheme positively influenced the final diagnoses made by 3 radiologists, consistently increasing accuracy rates. This promising result allows establishing this software as a valuable tool for radiologists in the analysis of masses in digital mammography. The scheme can be implemented on any operating system, or even accessed online. |
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Brazilian Journal of Health Review |
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Real time mass classification for mammographic images: a Driven CADx schemeCADx schemedigital mammographydigital mammogram processingmass classification in mammographyComputer-Aided Diagnosis (CADx) schemes have been proposed to serve as a supplementary image analysis tool in mammography. Experienced radiologists tend to be more assertive to such schemes in assisting their interpretation rather than solely relying on their ability to detect suspicious signals. This study focuses on a simplified version of a previously developed mammography CADx scheme, which was initially designed for digitized film, but is now specifically aimed at classifying breast nodules marked as regions of interest on digital images. This “driven” CADx scheme provides prompt indications regarding whether the selected nodule is deemed normal or suspicious. Its performance was evaluated through tests conducted on different mammograms sets – one with large number of images selected from DDSM database for training, testing and validation of classification parameters, and other comprising direct digital images from InBreast database. Remarkably, similar rates were observed for sensitivity, specificity and accuracy across these two sets (83%, 67% and 72%, respectively). The classification attributes were associated to contour, density and texture. Furthermore, a third test was conducted involving radiologists analyzing digital mammograms obtained from a specific full field digital mammography (FFDM) unit. Results showed that the Driven CADx scheme positively influenced the final diagnoses made by 3 radiologists, consistently increasing accuracy rates. This promising result allows establishing this software as a valuable tool for radiologists in the analysis of masses in digital mammography. The scheme can be implemented on any operating system, or even accessed online. Brazilian Journals Publicações de Periódicos e Editora Ltda.2023-06-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/6109510.34119/bjhrv6n3-429Brazilian Journal of Health Review; Vol. 6 No. 3 (2023); 13909-13927Brazilian Journal of Health Review; Vol. 6 Núm. 3 (2023); 13909-13927Brazilian Journal of Health Review; v. 6 n. 3 (2023); 13909-139272595-6825reponame:Brazilian Journal of Health Reviewinstname:Federação das Indústrias do Estado do Paraná (FIEP)instacron:BJRHenghttps://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/61095/44078Schiabel, HomeroMatheus, Bruno Roberto NepomucenoCardoso, Fernanda Junqueira Fortesinfo:eu-repo/semantics/openAccess2023-06-30T13:58:53Zoai:ojs2.ojs.brazilianjournals.com.br:article/61095Revistahttp://www.brazilianjournals.com/index.php/BJHR/indexPRIhttps://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/oai|| brazilianjhr@gmail.com2595-68252595-6825opendoar:2023-06-30T13:58:53Brazilian Journal of Health Review - Federação das Indústrias do Estado do Paraná (FIEP)false |
dc.title.none.fl_str_mv |
Real time mass classification for mammographic images: a Driven CADx scheme |
title |
Real time mass classification for mammographic images: a Driven CADx scheme |
spellingShingle |
Real time mass classification for mammographic images: a Driven CADx scheme Schiabel, Homero CADx scheme digital mammography digital mammogram processing mass classification in mammography |
title_short |
Real time mass classification for mammographic images: a Driven CADx scheme |
title_full |
Real time mass classification for mammographic images: a Driven CADx scheme |
title_fullStr |
Real time mass classification for mammographic images: a Driven CADx scheme |
title_full_unstemmed |
Real time mass classification for mammographic images: a Driven CADx scheme |
title_sort |
Real time mass classification for mammographic images: a Driven CADx scheme |
author |
Schiabel, Homero |
author_facet |
Schiabel, Homero Matheus, Bruno Roberto Nepomuceno Cardoso, Fernanda Junqueira Fortes |
author_role |
author |
author2 |
Matheus, Bruno Roberto Nepomuceno Cardoso, Fernanda Junqueira Fortes |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Schiabel, Homero Matheus, Bruno Roberto Nepomuceno Cardoso, Fernanda Junqueira Fortes |
dc.subject.por.fl_str_mv |
CADx scheme digital mammography digital mammogram processing mass classification in mammography |
topic |
CADx scheme digital mammography digital mammogram processing mass classification in mammography |
description |
Computer-Aided Diagnosis (CADx) schemes have been proposed to serve as a supplementary image analysis tool in mammography. Experienced radiologists tend to be more assertive to such schemes in assisting their interpretation rather than solely relying on their ability to detect suspicious signals. This study focuses on a simplified version of a previously developed mammography CADx scheme, which was initially designed for digitized film, but is now specifically aimed at classifying breast nodules marked as regions of interest on digital images. This “driven” CADx scheme provides prompt indications regarding whether the selected nodule is deemed normal or suspicious. Its performance was evaluated through tests conducted on different mammograms sets – one with large number of images selected from DDSM database for training, testing and validation of classification parameters, and other comprising direct digital images from InBreast database. Remarkably, similar rates were observed for sensitivity, specificity and accuracy across these two sets (83%, 67% and 72%, respectively). The classification attributes were associated to contour, density and texture. Furthermore, a third test was conducted involving radiologists analyzing digital mammograms obtained from a specific full field digital mammography (FFDM) unit. Results showed that the Driven CADx scheme positively influenced the final diagnoses made by 3 radiologists, consistently increasing accuracy rates. This promising result allows establishing this software as a valuable tool for radiologists in the analysis of masses in digital mammography. The scheme can be implemented on any operating system, or even accessed online. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-06-30 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/61095 10.34119/bjhrv6n3-429 |
url |
https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/61095 |
identifier_str_mv |
10.34119/bjhrv6n3-429 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/61095/44078 |
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 |
Brazilian Journals Publicações de Periódicos e Editora Ltda. |
publisher.none.fl_str_mv |
Brazilian Journals Publicações de Periódicos e Editora Ltda. |
dc.source.none.fl_str_mv |
Brazilian Journal of Health Review; Vol. 6 No. 3 (2023); 13909-13927 Brazilian Journal of Health Review; Vol. 6 Núm. 3 (2023); 13909-13927 Brazilian Journal of Health Review; v. 6 n. 3 (2023); 13909-13927 2595-6825 reponame:Brazilian Journal of Health Review instname:Federação das Indústrias do Estado do Paraná (FIEP) instacron:BJRH |
instname_str |
Federação das Indústrias do Estado do Paraná (FIEP) |
instacron_str |
BJRH |
institution |
BJRH |
reponame_str |
Brazilian Journal of Health Review |
collection |
Brazilian Journal of Health Review |
repository.name.fl_str_mv |
Brazilian Journal of Health Review - Federação das Indústrias do Estado do Paraná (FIEP) |
repository.mail.fl_str_mv |
|| brazilianjhr@gmail.com |
_version_ |
1797240031447875584 |