Evaluating the feasibility of using Generative Models to generate Chest
  X-Ray Data
Muhammad Danyal Malik Danish Humair
In this paper, we explore the feasibility of using generative models,
specifically Progressive Growing GANs (PG-GANs) and Stable Diffusion
fine-tuning, to generate synthetic chest X-ray images for medical diagnosis
purposes. Due to ethical concerns, obtaining sufficient medical data for
machine learning is a challenge, which our approach aims to address by
synthesising more data. We utilised the Chest X-ray 14 dataset for our
experiments and evaluated the performance of our models through qualitative and
quantitative analysis. Our results show that the generated images are visually
convincing and can be used to improve the accuracy of classification models.
However, further work is needed to address issues such as overfitting and the
limited availability of real data for training and testing. The potential of
our approach to contribute to more effective medical diagnosis through deep
learning is promising, and we believe that continued advancements in image
generation technology will lead to even more promising results in the future.