Generating X-Ray images of covid-19 using generative adversarial networks (Gans) and covid-19 detection using convolutional neural natworks (CNN)

dc.contributor.author	Ayed Nacereddine
dc.contributor.author	Aggoun Mohammed Amin
dc.contributor.author	Zouad Sara
dc.date.accessioned	2021-11-06T07:52:52Z
dc.date.available	2021-11-06T07:52:52Z
dc.date.issued	2021
dc.description.abstract	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.	ar
dc.identifier.uri	http://hdl.handle.net/123456789/11169
dc.language.iso	en	ar
dc.publisher	Université de Larbi Ben M’hidi-Oum Oum El Bouaghi	ar
dc.subject	Deep learning	ar
dc.subject	The generative approach	ar
dc.subject	Generative adversarial networks	ar
dc.title	Generating X-Ray images of covid-19 using generative adversarial networks (Gans) and covid-19 detection using convolutional neural natworks (CNN)	ar
dc.type	Other	ar

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