Titlebook: GANs for Data Augmentation in Healthcare; Arun Solanki,Mohd Naved Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusi

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?konomische Implikationen des Bosman-Urteilsersarial networks (GANs) have been employed for data augmentation for refining the deep learning models by generating additional information with no pre-planned process to generate realistic samples from the existing data and improve the model performance. Wasserstein Generative Adversarial Network

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Chest X-Ray Data Augmentation with Generative Adversarial Networks for Pneumonia and COVID-19 Diagnplement chest X-rays. We demonstrate that our GAN-based techniques for data augmentation outperforms previous traditional data augmentation techniques to train a GAN in identifying abnormalities in chest X-ray images by comparing our data augmentation GAN method with DCGAN (Deep Convolutional Genera

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State of the Art Framework-Based Detection of GAN-Generated Face Images, The inception-based model topped the list with a test accuracy of 99%. The ResNet and EfficientNet models were tied for second place with 97% testing accuracy. A separate five-fold-cross-validation method was also performed in comparison to the holdout method. Though this is a specific use case, we

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Geometric Transformations-Based Medical Image Augmentation,ion-based data augmentation segments the infected area and the classification process is proposed to highlight the severity of the disease. The proposed suggests an impartial and all-encompassing framework of evaluation for various information augmentation techniques. With this cutting-edge procedur

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