Browsing by Author "Ayed Nacereddine"

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    Generating X-Ray images of covid-19 using generative adversarial networks (Gans) and covid-19 detection using convolutional neural natworks (CNN)
    (Université de Larbi Ben M’hidi-Oum Oum El Bouaghi, 2021) Ayed Nacereddine; Aggoun Mohammed Amin; Zouad Sara
    Recently, the generative approach becomes one of the most important areas of deep learning. Deep learning, and generative adversarial networks are the new line of research in this field, GANs have proved their ability to generate high-resolution images and achieved great success in computer vision. We suppose that this tool will be able to generate new accurate images for the COVID-19 dataset to improve the diagnosis of the disease. The expected benefit of this work is to create a perception using deep convolutional generative adversarial networks (DCGANs) and convolutional neural networks (CNN), to explore the potential of this powerful deep learning tool to serve COVID-19 diagnosis. As an impact for further studies in the future, this work may offer insights on how to generate new images to help diagnose many diseases.