@InProceedings{VogadoVeAnArSiAi:2017:DiLeBl,
               author = "Vogado, Luis Henrique Silva and Veras, Rodrigo de Melo Souza and 
                         Andrade, Alan Ribeiro and Araujo, Flavio Henrique Duarte de and 
                         Silva, Romuere Rodrigues Veloso e and Aires, Kelson Romulo 
                         Teixeira",
          affiliation = "{Universidade Federal do Piau{\'{\i}}} and {Universidade Federal 
                         do Piau{\'{\i}}} and {Universidade Federal do Piau{\'{\i}}} 
                         and {Universidade Federal do Piau{\'{\i}}} and {Universidade 
                         Federal do Piau{\'{\i}}} and {Universidade Federal do 
                         Piau{\'{\i}}}",
                title = "Diagnosing Leukemia in Blood Smear Images Using an Ensemble of 
                         Classifiers and Pre-trained Convolutional Neural Networks",
            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 = "leukemia, computer-aided diagnosis, convolutional neural networks, 
                         transfer learning.",
             abstract = "Leukemia is a worldwide disease. In this paper we demonstrate that 
                         it is possible to build an automated, efficient and rapid leukemia 
                         diagnosis system. We demonstrate that it is possible to improve 
                         the precision of current techniques from the literature using the 
                         description power of well-known Convolutional Neural Networks 
                         (CNNs). We extract features from a blood smear image using 
                         pre-trained CNNs in order to obtain an unique image description. 
                         Many feature selection techniques were evaluated and we chose PCA 
                         to select the features that are in the final descriptor. To 
                         classify the images on healthy and pathological we created an 
                         ensemble of classifiers with three individual classification 
                         algorithms (Support Vector Machine, Multilayer Perceptron and 
                         Random Forest). In the tests we obtained an accuracy rate of 100%. 
                         Besides the high accuracy rate, the tests showed that our approach 
                         requires less processing time than the methods analyzed in this 
                         paper, considering the fact that our approach does not use 
                         segmentation to obtain specific cell regions from the blood smear 
                         image.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.55",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.55",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFPUKH",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFPUKH",
           targetfile = "PID4959787.pdf",
        urlaccessdate = "2024, May 02"
}