## 4 Results
### Image Classification Results
The performance difference decreases with FixResNeXt and VIT as close competitors, but there are differences in the validation precision: 71% for FixResNeXt and 75.47% for VIT, as seen in Figures 18 and 19. Figure 17: Performance evaluation of deep learning models on an augmented endoscopic dataset, showing substantially improved and more uniform performance across all models and metrics compared to both original and synthetic datasets, with most models achieving approximately 90% across all evaluation measures. The augmented dataset shows a complete transition. The result shows that ViT and DeiT are the most accurate models with a validation accuracy of 93% and having the highest f1 score of 92.89%. The other two architectures, FixResNeXt and BiT, follow slightly behind ViT and DeiT. In contrast, here the validation metrics look worse for the accuracy of EfficientNet (86%). In particular, most customer variants demonstrate improved accuracy in the augmented dataset compared to the synthetic and original datasets, thus containing more learnable patterns. The Vision Transformer (ViT) demonstrates remarkable proficiency in polyp detection in both real and synthetic colonoscopy images, as shown in the figure. The model, trained on an augmented dataset that combined real colonoscopy images and synthetic data generated through fine-tuned Stable Diffusion, processes the input images by dividing them into fixed-size patches that are linearly embedded and processed through transformer encoder blocks. This training approach enables the ViT to learn robust features common to both real and synthetic polyps, preventing overfitting to specific real-world image characteristics. The model's effectiveness is evident in its high-confidence predictions: accurately identifying a subtle polyp in the real image with 0.95 confidence and a more  
Figure 19: Validated F1 Score comparison across the 3 datasets. Figure 18: Validated accuracy comparison across the 3 datasets. prominent polyp in the synthetic image with 0.99 confidence. The ViT generates appropriate bounding boxes in both cases, a smaller and precise box for the real polyp and a larger box that encompasses the full structure of the synthetic polyp, validating the effectiveness of synthetic data enhancement in medical image analysis, as seen in Figure 20.  
The difference between training and validation scores, which affect a model's capacity to generalize, is the smallest for the augmented dataset. By comparing the outcomes, the results show that EfficientNet, one of the most variable networks, performs worst in intelligent dataset but better in the original dataset. Conversely, in the augmented dataset, the transformer-based structure (DeiT, ViT) performs much better overall. Intelligent Data Augmentation produced the best overall results for the following reasons:  
1. **High Performance** The augmented dataset demonstrated exceptional performance, consistently achieving accuracy metrics above 92% in different models and evaluation metrics. This indicates robust and reliable model behavior across various architectural implementations. 2. **Performance Consistency** A notable pattern emerged in the comparative analysis. The original dataset showed inconsistent performance, with only one model achieving high accuracy (EfficientNet with 97% validation accuracy). The synthetic dataset consistently showed lower performance across all models. In contrast, the augmented dataset maintained consistently high performance across multiple architectures, indicating improved data quality and representation. 3. **Strong Generalization** The minimal gap between training and validation accuracy in the augmented dataset (typically within 2-3 percentage points) indicates effective knowledge transfer, reduced overfitting, and robust model generalization capabilities. 4. **Architecture-Agnostic Performance** Unlike the original and synthetic datasets, which showed significant performance variations between different architectures, the augmented dataset demonstrated more balanced performance.