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Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2021: administrator
Citation KeyJúniorAfonPalmPapa:2017:BaEsId
TitleBarrett’s Esophagus Identification Using Optimum-Path Forest
Access Date2021, Mar. 02
Number of Files1
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Author1 Júnior, Luis Antonio de Souza
2 Afonso, Luis Cláudio Sugi
3 Palm, Christoph
4 Papa, João Paulo
Affiliation1 Federal University of São Carlos - UFScar
2 Federal University of São Carlos - UFScar
3 Ostbayerische Technische Hochschule Regensburg
4 São Paulo State University - UNESP
EditorTorchelsen, 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
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-18 03:29:56 :: -> administrator ::
2021-02-23 03:52:58 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsbarrett's esophagus, machine learning, pattern recognition.
AbstractComputer-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.
ArrangementSIBGRAPI 2017 > Barretts Esophagus Identification...
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