  Biomedical image datasets can be imbalanced due to the rarity of targeted
diseases. Generative Adversarial Networks play a key role in addressing this
imbalance by enabling the generation of synthetic images to augment datasets.
It is important to generate synthetic images that incorporate a diverse range
of features to accurately represent the distribution of features present in the
training imagery. Furthermore, the absence of diverse features in synthetic
images can degrade the performance of machine learning classifiers. The mode
collapse problem can impact a Generative Adversarial Network's capacity to
generate diversified images. Mode collapse comes in two varieties: intra-class
and inter-class. In this paper, the intra-class mode collapse problem is
investigated, and its subsequent impact on the diversity of synthetic X-ray
images is evaluated. This work contributes an empirical demonstration of the
benefits of integrating the adaptive input-image normalization for the Deep
Convolutional GAN to alleviate the intra-class mode collapse problem. Results
demonstrate that the DCGAN with adaptive input-image normalization outperforms
DCGAN with un-normalized X-ray images as evident by the superior diversity
scores.
