## 5 Conclusions  
This study presents a novel enhancement to the StarGAN architecture for endoscopic image augmentation through the integration of Perspective Transformation and Viewpoint Attention Modules. Our approach addresses the critical challenges of generating diverse and diagnostically relevant synthetic medical images while maintaining essential clinical features.  
Experimental results demonstrate consistent improvements across all evaluated deep learning architectures, with accuracy gains ranging from 0.298 to 0.704 percentage points. The most substantial improvements were observed in older architectures like VGG-16, suggesting that our augmentation approach particularly benefits less complex networks. Meanwhile, EfficientNet-B7 achieved the highest overall accuracy of 95.25% when trained with our enhanced augmentation.  
Qualitative analysis reveals that our model produces more varied images than the original StarGAN by introducing diversity in viewing angles and feature emphasis while  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Reported Accuracy** & **Achieved Accuracy** & **Improvement** & **Confidence Interval** \\ \hline VGG-16 & 0.9343 & 0.9413 & +0.704 & \(\pm\)0.118 \\ \hline ResNet-50 & 0.9418 & 0.9468 & +0.504 & \(\pm\)0.106 \\ \hline DenseNet-121 & 0.9450 & 0.9481 & +0.318 & \(\pm\)0.131 \\ \hline InceptionNet-V3 & 0.9493 & 0.9522 & +0.298 & \(\pm\)0.082 \\ \hline EfficientNet-B7 & 0.9462 & 0.9525 & +0.636 & \(\pm\)0.097 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment results and comparison to previous paper.  
preserving diagnostic characteristics. This variability better represents the conditions encountered in real endoscopic examinations and addresses the persistent challenge of data scarcity in medical imaging applications.  
Our Enhanced StarGAN architecture represents a meaningful advancement in medical image augmentation, particularly for endoscopic applications where viewpoint variations are common. By improving the quality and diversity of synthetic training data, this approach contributes to the development of more accurate and robust computer-aided diagnostic systems, with potential benefits extending across various medical imaging domains.  
Future work should explore more advanced geometric transformations within our GAN framework, particularly incorporating controlled shearing and non-rigid deformations to better simulate the elastic properties of gastrointestinal tissues. Developing condition-specific transformation parameters could enhance the clinical relevance of generated samples by applying different degrees of perspective distortion based on anatomical region or pathology. Combining our approach with style-based GANs may also improve control over important visual characteristics like illumination and tissue coloration that vary considerably in real endoscopic examinations, further addressing data scarcity while generating increasingly realistic synthetic images for model training.  
Conceptualization and methodology, L.J. and D.S.; software, L.J.; writing--original draft preparation, L.J.; visualization, investigation, and editing, L.J. and D.S.; writing--review, supervision, project administration, and funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The Kvasir v2 dataset used in this study is available at [[https://www.kaggle.com/datasets/plhlavorsen/kvasir-v2-a-gastrointestinal-tract-dataset](https://www.kaggle.com/datasets/plhlavorsen/kvasir-v2-a-gastrointestinal-tract-dataset)]([https://www.kaggle.com/datasets/plhlavorsen/kvasir-v2-a-gastrointestinal-tract-dataset](https://www.kaggle.com/datasets/plhlavorsen/kvasir-v2-a-gastrointestinal-tract-dataset)) (accessed on 14 May 2025). Any additional data presented in this study are available on request from the corresponding authors.  
The authors declare no conflicts of interest.