A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Evaluation of Deep Convolutional Generative Adversarial Networks for Data Augmentation of Chest X-ray Images
2020
Future Internet
Medical image datasets are usually imbalanced due to the high costs of obtaining the data and time-consuming annotations. Training a deep neural network model on such datasets to accurately classify the medical condition does not yield the desired results as they often over-fit the majority class samples' data. Data augmentation is often performed on the training data to address the issue by position augmentation techniques such as scaling, cropping, flipping, padding, rotation, translation,
doi:10.3390/fi13010008
fatcat:bbjubtcubjajxbsghcr4h4ljua