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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WGAN for Data Augmentation,ersarial 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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Book 2023using GAN can be used to provide clear images of brain, liver, chest, abdomen, and liver on an MRI. In addition, GAN shows strong promise in the field of clinical image synthesis. In many cases, clinical evaluation is limited by a lack of data and/or the cost of actual information. GAN can overcome

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Data Augmentation in Classifying Chest Radiograph Images (CXR) Using DCGAN-CNN,

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?konomie der Abwasserbeseitigunging reasons of this abnormal illness include excessive blood cell proliferation and immature blood cell growth, which can affect red blood cells, bone marrow, and the immune system. Leukocytes are prominent factor in early detection to identify the diagnosis of leukemia which is early sign of illnes

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Christoph Kochhan,Jennifer Bannert together with generator. Generator generates the data which resembles the actual data and discriminator differentiates between actual data and generated data. Due to GAN’s complex structure, it becomes very hard to train it and it faces a lot of problems. Among these problems mode collapse is a ver

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?konomie der Geschlechterdifferenzphthalmology analysis of retinal networks gives information about the status and health condition of the eyes. The entire visual system is threatened by retinal illnesses such as retinal artery and vein occlusion, which can be prevented with early detection. Many supervised and unsupervised practice

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