Real time mass classification for mammographic images: a Driven CADx scheme

Detalhes bibliográficos
Autor(a) principal: Schiabel, Homero
Data de Publicação: 2023
Outros Autores: Matheus, Bruno Roberto Nepomuceno, Cardoso, Fernanda Junqueira Fortes
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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spelling 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
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