@article{MAKHILLJEAS2017122014928,
    title = {Implementation of a Data Augmentation Algorithm Validated by Means of the
Accuracy of a Convolutional Neural Network},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {20},
    pages = {5323-5331},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.5323.5331},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.5323.5331},
    author = {Paula Catalina,Robinson Jimenez and},
    keywords = {confusion matrix,layer activations,data augmentation,Deep convolutional neural network,new database,images},
    abstract = {The following study presents the validation of an application developed in MATLAB<sup>&reg;</sup> for data
augmentation which allows to improve the training of convolutional neural networks. The validation is done
by comparing the accuracy percentages in the prediction of a trained convolutional neural network with five
databases augmented in a different way which allows to determine the characteristics of the training images that
produce an increase in network recognition capacities. Each network trained was evaluated by confusion
matrices and compared their activations against a test image where it was found that the network with the
greatest recognition capacity depends on the changes generated by the data augmentation in the original
images (rotations, crops, background changes) as well as the ratio of augmented images and the number of
original images used by the data augmentation algorithm developed to produce a new database.}
    }