Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 116, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2019.103542
Keywords
Convolutional neural networks; HEp-2 cells; Staining pattern classification; Preprocessing; Data augmentation; Hyperparameters; Fine-tuning
Categories
Funding
- NVIDIA Corporation, Brazil
- CAPES, Brazil
- FUNARBE, Brazil
- FAPEMIG, Brazil [CEX - APQ-02964-17]
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) [001]
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Autoimmune diseases are the third highest cause of mortality in the world, and the identification of an antinuclear antibody via an immunofluorescence test for HEp-2 cells is a standard procedure to support diagnosis. In this work, we assess the performance of six preprocessing strategies and five state-of-the-art convolutional neural network architectures for the classification of HEp-2 cells. We also evaluate enhancement methods such as hyperparameter optimization, data augmentation, and fine-tuning training strategies. All experiments were validated using a five-fold cross-validation procedure over the training and test sets. In terms of accuracy, the best result was achieved by training the Inception-V3 model from scratch, without preprocessing and using data augmentation (98.28%). The results suggest the conclusions that most CNNs perform better on non-preprocessed images when trained from scratch on the analyzed dataset, and that data augmentation can improve the results from all models. Although fine-tuning training did not improve the accuracy compared to training the CNNs from scratch, it successfully reduced the training time.
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