A snapshot of image pre-processing for convolutional neural networks: case study of MNIST Tabik, Siham Peralta, Daniel Herrera-Poyatos, Andrés Herrera Triguero, Francisco Classification Deep learning Convolutional Neural Networks (CNNs) Preprocessing Handwritten digits Data augmentation In the last five years, deep learning methods and particularly Convolutional Neural Networks (CNNs) have exhibited excellent accuracies in many pattern classification problems. Most of the state-of-the-art models apply data-augmentation techniques at the training stage. This paper provides a brief tutorial on data preprocessing and shows its benefits by using the competitive MNIST handwritten digits classification problem. We show and analyze the impact of different preprocessing techniques on the performance of three CNNs, LeNet, Network3 and DropConnect, together with their ensembles. The analyzed transformations are, centering, elastic deformation, translation, rotation and different combinations of them. Our analysis demonstrates that data-preprocessing techniques, such as the combination of elastic deformation and rotation, together with ensembles have a high potential to further improve the state-of-the-art accuracy in MNIST classification. 2020-12-23T10:27:29Z 2020-12-23T10:27:29Z 2017-01 journal article Tabik, S., Peralta, D., Herrera-Poyatos, A., & Herrera, F. (2017). A snapshot of image pre-processing for convolutional neural networks: case study of MNIST. International Journal of Computational Intelligence Systems, 10(1), 555-568. [https://doi.org/10.2991/ijcis.2017.10.1.38] http://hdl.handle.net/10481/65129 10.2991/ijcis.2017.10.1.38 eng http://creativecommons.org/licenses/by-nc/3.0/es/ open access Atribución-NoComercial 3.0 España Atlantis Press