@InProceedings{RodriguesNaldMari:2017:ExCoNe,
               author = "Rodrigues, Larissa Ferreira and Naldi, Murilo Coelho and Mari, 
                         Jo{\~a}o Fernando",
          affiliation = "{Universidade Federal de Vi{\c{c}}osa} and {Universidade Federal 
                         de Vi{\c{c}}osa} and {Universidade Federal de Vi{\c{c}}osa}",
                title = "Exploiting Convolutional Neural Networks and preprocessing 
                         techniques for HEp-2 cell classification in immunofluorescence 
                         images",
            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 = "Convolutional neural networks, HEp-2 cells, staining patterns 
                         classification, LeNet-5, AlexNet, GoogLeNet, pre-processing, data 
                         augmentation.",
             abstract = "Autoimmune diseases are the third cause of mortality in the world. 
                         The identification of anti-nuclear antibody (ANA) via 
                         Immunofluorescence (IIF) test in human epithelial type-2 cells 
                         (HEp-2) is a conventional method to support the diagnosis of such 
                         diseases. In the present work, three popular Convolutional Neural 
                         Networks (CNNs) are evaluated for this task: LeNet-5, AlexNet, and 
                         GoogLeNet. We also assess the impact of six different 
                         pre-processing strategies on the performance of these CNNs. 
                         Additionally, data augmentation based on the rotation of the 
                         training set images after the pre-processing strategies was 
                         evaluated. Our work is the first to consider AlexNet and GoogLeNet 
                         models for the proposed analysis and classification of HEp-2 cells 
                         images, besides the LeNet-5. Experimental results allow to 
                         conclude that neither pre-processing strategies were essential to 
                         improve accuracy values of the CNNs. However, when data 
                         augmentation is considered, contrast enhancement followed by data 
                         centralization is significant in order to achieve good results. 
                         Additionally, our results were compared with results from other 
                         state-of-art papers. Our best results were achieved by GoogLeNet 
                         architecture trained with images with no pre-processing and no 
                         data augmentation, resulting in 98.17% of accuracy, which 
                         outperforms the results presented in other works in literature.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.29",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.29",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFR4G8",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFR4G8",
           targetfile = "PID4960235.pdf",
        urlaccessdate = "2024, May 02"
}