Optimizing data augmentation policies for convolutional neural networks based on classification of sickle cells

Data augmentation is a key procedure in many image classification tasks, mainly to overcome the problem of small datasets. In this work, we exploit the data augmentation as a hyperparameter optimization approach. We tested our methods to classify erythrocytes to assist the diagnosis of sickle cell anemia. In this study, we proposed a data augmentation approach based on hyperparameter optimization to find the best augmentation policies through the Bayesian optimization algorithm. We also developed a convolutional neural network architecture from scratch and compared it with two classic architectures to classify sickle cell images. Our approach defines the best data augmentation solutions and sends those solutions to be carried out by CNN in the final training. All experiments were validated using a stratified five-fold cross-validation procedure, and our best result achieves 92.54% of accuracy. The results suggest the best augmentation policies defined with optimization allow us to obtain superior results than other strategies such as without data augmentation, several randomly defined image transformations, and only random rotations. As far as we know, our paper is the first to propose optimizing data augmentation policies in biomedical images leading to a better exploration of these strategies in several fields.