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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RPBCBB
Repositorysid.inpe.br/sibgrapi/2018/09.04.01.45
Last Update2018:09.04.01.45.34 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/09.04.01.45.34
Metadata Last Update2022:06.14.00.09.24 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00029
Citation KeyBaffaLatt:2018:CoNeNe
TitleConvolutional Neural Networks for Static and Dynamic Breast Infrared Imaging Classification
FormatOn-line
Year2018
Access Date2024, Apr. 29
Number of Files1
Size6091 KiB
2. Context
Author1 Baffa, Matheus de Freitas Oliveira
2 Lattari, Lucas Grassano
Affiliation1 Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais
2 Instituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressmfreitas826@gmail.com
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-09-04 01:45:34 :: mfreitas826@gmail.com -> administrator ::
2022-06-14 00:09:24 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsbreast cancer
computer-aided diagnosis
convolutional neural network
deep learning
AbstractBreast cancer is the most frequent type of cancer among women. Since early diagnosis provides a better prognosis, different techniques have been developed by researchers all over the world. Several studies proved the efficiency of infrared image as a breast cancer screening technique. This paper proposes a methodology for analyzing infrared thermography of breast, considering distinct protocols, in order to classify patients images as healthy or non-healthy due to anomalies such as cancer. The major contribution of this work is to provide accurate classification using Convolutional Neural Networks, which were not exploited in previous works. Many methods relies on handcrafted features and traditional classificators, such as Support Vector Machines. We obtained competitive results compared to other works and we design an appropriate modelling which takes advantage of this type of deep learning architecture. Our proposal obtained 98% of accuracy for static protocol and 95% for dynamic protocol.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > Convolutional Neural Networks...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Convolutional Neural Networks...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RPBCBB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RPBCBB
Languageen
Target File96.pdf
User Groupmfreitas826@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 10
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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