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@InProceedings{AndradePaivSilv:2017:AnImTe,
               author = "Andrade, Felipe Jord{\~a}o Pinheiro de and Paiva, Anselmo Cardoso 
                         and Silva, Arist{\'o}fanes Corr{\^e}a",
          affiliation = "{Universidade Federal do Maranh{\~a}o} and {Universidade Federal 
                         do Maranh{\~a}o} and {Universidade Federal do Maranh{\~a}o}",
                title = "An{\'a}lise de Imagens de Termografia Din{\^a}mica para 
                         Classifica{\c{c}}{\~a}o de Altera{\c{c}}{\~o}es na Mama Usando 
                         S{\'e}ries Temporais",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Termografia Din{\^a}mica, S{\'e}ries Temporais.",
             abstract = "With the increase in the number of cases of breast cancer in the 
                         last years, the need for auxiliary techniques for the detection of 
                         the disease is evident. Dynamic thermography can be used as an 
                         auxiliary method to the gold standard, the mammography screening. 
                         The thermography exam takes advantage of the fact that the lesions 
                         present a higher temperature than the healthy neighboring tissues. 
                         In this work, we propose a methodology for the transformation of 
                         thermal signals into time series, from which features for the 
                         classification task will be extracted. In the paper we compare the 
                         K-Star and Support Vector Machine classifiers with results of 
                         95.8% accuracy, 93.6% sensitivity and 95.9% specificity.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3PJ5TCS",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJ5TCS",
           targetfile = "CameraReady_Sibigrapi_termica.pdf",
        urlaccessdate = "2021, Jan. 26"
}


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