%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.21.20.07
%2 sid.inpe.br/sibgrapi/2017/08.21.20.07.39
%@doi 10.1109/SIBGRAPI.2017.29
%T Exploiting Convolutional Neural Networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images
%D 2017
%A Rodrigues, Larissa Ferreira,
%A Naldi, Murilo Coelho,
%A Mari, João Fernando,
%@affiliation Universidade Federal de Viçosa
%@affiliation Universidade Federal de Viçosa
%@affiliation Universidade Federal de Viçosa
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ, Brazil
%8 17-20 Oct. 2017
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Convolutional neural networks, HEp-2 cells, staining patterns classification, LeNet-5, AlexNet, GoogLeNet, pre-processing, data augmentation.
%X 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.
%@language en
%3 PID4960235.pdf