author = "Baffa, Matheus de Freitas Oliveira and Lattari, Lucas Grassano",
          affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e 
                         Tecnologia do Sudeste de Minas Gerais and Instituto Federal de 
                         Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia do Sudeste de 
                         Minas Gerais",
                title = "Convolutional Neural Networks for Static and Dynamic Breast 
                         Infrared Imaging Classification",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "breast cancer, computer-aided diagnosis, convolutional neural 
                         network, deep learning.",
             abstract = "Breast cancer is the most frequent type of cancer among women. 
                         Since early diagnosis provides a better prognosis, different 
                         techniques have been developed by researchers all over the world. 
                         Several studies proved the efficiency of infrared image as a 
                         breast cancer screening technique. This paper proposes a 
                         methodology for analyzing infrared thermography of breast, 
                         considering distinct protocols, in order to classify patients 
                         images as healthy or non-healthy due to anomalies such as cancer. 
                         The major contribution of this work is to provide accurate 
                         classification using Convolutional Neural Networks, which were not 
                         exploited in previous works. Many methods relies on handcrafted 
                         features and traditional classificators, such as Support Vector 
                         Machines. We obtained competitive results compared to other works 
                         and we design an appropriate modelling which takes advantage of 
                         this type of deep learning architecture. Our proposal obtained 98% 
                         of accuracy for static protocol and 95% for dynamic protocol.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "96.pdf",
        urlaccessdate = "2020, Dec. 04"