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Large medical datasets are crucial for advancing contemporary medical practices that incorporate computer vision
and machine learning techniques. These records serve as indispensable resources for identifying patterns that assist
healthcare professionals in diagnosing rare diseases and enhancing patient outcomes. Moreover, these datasets drive
research into the causes and progression of such diseases, potentially leading to innovative therapeutic strategies.
However, the acquisition of such data poses significant challenges due to privacy and ethical concerns, as well as
the rarity of certain conditions. Therefore, it is imperative to both collect new medical data and develop tools that
facilitate the enhancement of existing datasets while preserving the accurate characteristics of the diseases. This study
focuses on leveraging deep convolutional generative adversarial networks (DCGAN) to expand a dataset containing
images of retinitis pigmentosa, a rare eye condition affecting the retina. Our study showcases that integrating xtreme
gradient boosting (XGBoost) within the DCGAN framework enhances the clarity and quality of these augmented
images. By employing hybrid VGG16 alongside XGBoost techniques during training, we observe improvements in
detection accuracy. The outcomes of the proposed method are highly promising, with the model achieving all key per-
formance metrics surpassing the 90% threshold as well as improving baseline classification accuracy by almost 19%.
