I am studying the performance of deep learning models toward abnormality detection in chest X-rays.
Due to sparsity of data, I augment the data using different augmentation strategies including:
- Traditional augmentation methods (Gaussian smoothing, unsharp masking, and minimum filtering)
- Generative Adversarial Networks
Contrary to the existing literature, I find that the models showed promising results with traditional augmentation methods (that i have mentioned herewith) than with GAN-generated synthetic images.
What brings this performance difference?