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@InProceedings{JúniorAfonPalmPapa:2017:BaEsId,
               author = "J{\'u}nior, Luis Antonio de Souza and Afonso, Luis Cl{\'a}udio 
                         Sugi and Palm, Christoph and Papa, Jo{\~a}o Paulo",
          affiliation = "{Federal University of S{\~a}o Carlos - UFScar} and {Federal 
                         University of S{\~a}o Carlos - UFScar} and {Ostbayerische 
                         Technische Hochschule Regensburg} and {S{\~a}o Paulo State 
                         University - UNESP}",
                title = "Barrett’s Esophagus Identification Using Optimum-Path Forest",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             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.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4956031.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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