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%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.18.03.29
%2 sid.inpe.br/sibgrapi/2017/08.18.03.29.56
%T Barrett’s Esophagus Identification Using Optimum-Path Forest
%D 2017
%8 Oct. 17-20, 2017
%A Júnior, Luis Antonio de Souza,
%A Afonso, Luis Cláudio Sugi,
%A Palm, Christoph,
%A Papa, João Paulo,
%@affiliation Federal University of São Carlos - UFScar
%@affiliation Federal University of São Carlos - UFScar
%@affiliation Ostbayerische Technische Hochschule Regensburg
%@affiliation São Paulo State University - UNESP
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%S Proceedings
%I IEEE Computer Society
%J Los Alamitos
%K barrett's esophagus, machine learning, pattern recognition.
%X 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.
%@language en
%3 PID4956031.pdf


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