Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images

Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentatio...

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Autores principales:	Saman Motamed, Patrik Rogalla, Farzad Khalvati
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Data augmentation Semi-supervised learning Generative adversarial networks Disease detection Medical Imaging Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/5840f55202eb42e8ad8690506a751581
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Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images

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