## 1 Introduction
This data scarcity problem leads to imbalanced datasets causing overfitting, biased results, and diminished diagnostic accuracy [9]. The challenge is particularly pronounced when attempting to develop computer-aided diagnosis systems for different organs using images from various modalities, as each presents unique characteristics requiring specialized analytical approaches [8;9]. While data augmentation techniques have emerged as a common solution to address these limitations, the effectiveness of specific augmentation methods varies significantly depending on the disease type and neural network architecture employed. Unlike conventional computer-aided detection schemes that require manual feature development, deep learning models can progressively identify and learn hidden patterns inside regions of interest through their hierarchical architecture, potentially overcoming these data limitations when properly implemented [8]. Among various deep learning architectures, Generative Adversarial Networks (GANs) have emerged as particularly powerful tools for medical image generation, with recent studies demonstrating their capacity to produce realistic synthetic images that can effectively augment limited training datasets while preserving clinically relevant features [10]. GANs operate through a competitive framework between two neural networks: a generator that creates synthetic images and a discriminator that attempts to distinguish real from generated data. This adversarial training process enables GANs to produce realistic medical images over time. In the scope of healthcare, GANs address fundamental challenges posed by limited dataset availability, privacy restrictions, and imbalanced class distributions. GAN-based augmentation extends beyond simple transformations to generate entirely new samples based on learned data distributions, providing enhanced diversity and quality of training datasets. Various GAN architectures have been successfully implemented across different medical imaging modalities. However, there are persistent challenges with GAN implementations, including mode collapse, gradient vanishing problems, and difficulties in maintaining training stability, emphasizing the need for continued research to optimize these approaches for medical imaging applications [11]. Previous research has explored GAN architectures for endoscopic image augmentation, with StarGAN demonstrating particular promise in this domain [12]. StarGAN addresses a critical limitation of conventional GANs by enabling multi-domain image-to-image translation using only a single model architecture [13], which significantly improves scalability for medical imaging applications. GAN-based augmentation can substantially enhance the classification performance of endoscopic imaging systems. Their research validated the effectiveness of applying data augmentation based on generative networks to endoscopic images of various gastrointestinal conditions. Their study empirically demonstrated that StarGAN augmentation yielded superior outcomes compared to traditional augmentation methods when applied to datasets showing different types of gastrointestinal diseases. This work established a foundation, and our current research builds upon it, introducing further architectural refinements to address the unique challenges posed by endoscopic image synthesis [12]. This study advances GAN-based endoscopic image synthesis by modifying the StarGAN architecture to address domain-specific challenges in gastrointestinal imaging. Our approach introduces specialized perspective and viewpoint transformation layers to the StarGAN model, enabling the network to generate synthetic endoscopic images that better account for the unique positional variations encountered during actual endoscopic procedures.