Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPAW/3PF8RQE |
Repository | sid.inpe.br/sibgrapi/2017/08.18.03.29 |
Last Update | 2017:08.18.03.29.56 administrator |
Metadata | sid.inpe.br/sibgrapi/2017/08.18.03.29.56 |
Metadata Last Update | 2021:02.23.03.52.58 administrator |
Citation Key | JúniorAfonPalmPapa:2017:BaEsId |
Title | Barrett’s Esophagus Identification Using Optimum-Path Forest  |
Format | On-line |
Year | 2017 |
Access Date | 2021, Mar. 02 |
Number of Files | 1 |
Size | 404 KiB |
Context area | |
Author | 1 Júnior, Luis Antonio de Souza 2 Afonso, Luis Cláudio Sugi 3 Palm, Christoph 4 Papa, João Paulo |
Affiliation | 1 Federal University of São Carlos - UFScar 2 Federal University of São Carlos - UFScar 3 Ostbayerische Technische Hochschule Regensburg 4 São Paulo State University - UNESP |
Editor | Torchelsen, Rafael Piccin Nascimento, Erickson Rangel do Panozzo, Daniele Liu, Zicheng Farias, Mylène Viera, Thales Sacht, Leonardo Ferreira, Nivan Comba, João Luiz Dihl Hirata, Nina Schiavon Porto, Marcelo Vital, Creto Pagot, Christian Azambuja Petronetto, Fabiano Clua, Esteban Cardeal, Flávio |
e-Mail Address | luis.souza@dc.ufscar.br |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ |
Date | Oct. 17-20, 2017 |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2017-08-18 03:29:56 :: luis.souza@dc.ufscar.br -> administrator :: 2021-02-23 03:52:58 :: administrator -> :: 2017 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | barrett's esophagus, machine learning, pattern recognition. |
Abstract | Computer-assisted analysis of endoscopic images can be helpful to the automatic diagnosis and classification of neoplastic lesions. Barretts esophagus (BE) is a common type of reflux that is not straightforward to be detected by endoscopic surveillance, thus being way susceptible to erroneous diagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest (OPF) classifier to the task of automatic identification of Barretts esophagus, with promising results and outperforming the wellknown Support Vector Machines (SVM) in the aforementioned context. We consider describing endoscopic images by means of feature extractors based on key point information, such as the Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT), for further designing a bag-of-visual-words that is used to feed both OPF and SVM classifiers. The best results were obtained by means of the OPF classifier for both feature extractors, with values lying on 0.732 (SURF) - 0.735 (SIFT) for sensitivity, 0.782 (SURF) - 0.806 (SIFT) for specificity, and 0.738 (SURF) - 0.732 (SIFT) for the accuracy. |
Arrangement | |
source Directory Content | there are no files |
agreement Directory Content | |
Conditions of access and use area | |
data URL | http://urlib.net/rep/8JMKD3MGPAW/3PF8RQE |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PF8RQE |
Language | en |
Target File | PID4956031.pdf |
User Group | luis.souza@dc.ufscar.br |
Visibility | shown |
Update Permission | not transferred |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PKCC58 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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