  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 impacts Generative Adversarial Networks' capacity to generate
diversified images. Mode collapse comes in two varieties: intra-class and
inter-class. In this paper, both varieties of the mode collapse problem are
investigated, and their 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 with the Deep
Convolutional GAN and Auxiliary Classifier GAN to alleviate the mode collapse
problems. Synthetically generated images are utilized for data augmentation and
training a Vision Transformer model. The classification performance of the
model is evaluated using accuracy, recall, and precision scores. Results
demonstrate that the DCGAN and the ACGAN with adaptive input-image
normalization outperform the DCGAN and ACGAN with un-normalized X-ray images as
evidenced by the superior diversity scores and classification scores.
