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Biomedical image datasets can be imbalanced due to the rarity of targeteddiseases. Generative Adversarial Networks play a key role in addressing thisimbalance by enabling the generation of synthetic images to augment datasets.It is important to generate synthetic images that incorporate a diverse rangeof features to accurately represent the distribution of features present in thetraining imagery. Furthermore, the absence of diverse features in syntheticimages can degrade the performance of machine learning classifiers. The modecollapse problem impacts Generative Adversarial Networks' capacity to generatediversified images. Mode collapse comes in two varieties: intra-class andinter-class. In this paper, both varieties of the mode collapse problem areinvestigated, and their subsequent impact on the diversity of synthetic X-rayimages is evaluated. This work contributes an empirical demonstration of thebenefits of integrating the adaptive input-image normalization with the DeepConvolutional GAN and Auxiliary Classifier GAN to alleviate the mode collapseproblems. Synthetically generated images are utilized for data augmentation andtraining a Vision Transformer model. The classification performance of themodel is evaluated using accuracy, recall, and precision scores. Resultsdemonstrate that the DCGAN and the ACGAN with adaptive input-imagenormalization outperform the DCGAN and ACGAN with un-normalized X-ray images asevidenced by the superior diversity scores and classification scores.

Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images