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  • A comprehensive survey of recent trends in deep learning for digital images augmentation

    Author(s)
    Khalifa, NE
    Loey, M
    Mirjalili, S
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2021
    Metadata
    Show full item record
    Abstract
    Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. ...
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    Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. This pandemic was the main motivation of this survey to deliver and discuss the current image data augmentation techniques which can be used to increase the number of images. In this paper, a survey of data augmentation for digital images in deep learning will be presented. The study begins and with the introduction section, which reflects the importance of data augmentation in general. The classical image data augmentation taxonomy and photometric transformation will be presented in the second section. The third section will illustrate the deep learning image data augmentation. Finally, the fourth section will survey the state of the art of using image data augmentation techniques in the different deep learning research and application.
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    Journal Title
    Artificial Intelligence Review
    DOI
    https://doi.org/10.1007/s10462-021-10066-4
    Note
    This publication has been entered in Griffith Research Online as an advanced online version.
    Subject
    Artificial intelligence
    Cognitive neuroscience
    Publication URI
    http://hdl.handle.net/10072/407990
    Collection
    • Journal articles

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