## 4 Experiments and Results
### Experiment Results  
Experimental evaluation was conducted across five state-of-the-art deep learning architectures: VGG-16, ResNet-50, DenseNet-121, InceptionNet-V3, and EfficientNet-B7. All models were trained for the multiclass classification task involving all eight classes from the Kvasir v2 dataset: anatomical landmarks (normal cecum, normal pylorus, and normal Z-line), pathological findings (esophagitis, polyps, and ulcerative colitis), and polyp removal process images (dyed lifted polyps and dyed resection margins). As mentioned before, the dataset was augmented by generating an additional image to the training dataset, using our Enhanced GAN model.  
Table 2 presents results comparing the performance of five state-of-the-art deep learning architectures with reported accuracy in reference [12] and after data augmentation using an Enhanced GAN model. The reported confidence intervals are based on 20 runs of training made for each model architecture, ensuring robust statistical validation of the observed improvements. Below is an analysis of the results:  
* All five models (VGG-16, ResNet-50, DenseNet-121, InceptionNet-V3, and EfficientNet-B7) showed improvements in accuracy after augmenting the training dataset with additional GAN-generated images.
* VGG-16 showed the largest relative improvement, increasing from 93.43% to 94.13%, resulting in a gain of 0.704 percentage points with a confidence interval of \(\pm\)0.118.
* ResNet-50 improved from 94.18% to 94.68%, with a gain of 0.504 percentage points (\(\pm\)0.106).
* DenseNet-121 improved from 94.50% to 94.81%, with a gain of 0.318 percentage points (\(\pm\)0.131).  
Figure 4: Comparison of generated images.  
* InceptionNet-V3 had the highest baseline accuracy and achieved 95.22% after augmentation, a gain of 0.298 percentage points (\(\pm\)0.082). This model had the narrowest confidence interval, suggesting more consistent improvement.
* EfficientNet-B7 showed the second-largest improvement and achieved the highest overall accuracy after augmentation (95.25%), with a gain of 0.636 percentage points (\(\pm\)0.097).  
These results demonstrate that the Enhanced GAN-based data augmentation consistently improved classification performance across different model architectures, with improvements ranging from approximately 0.3 to 0.7 percentage points. The confidence intervals indicate that all improvements are statistically significant, as none of them include zero.  
The observed improvements across all tested architectures highlight several important findings. First, the fact that older architectures like VGG-16 showed the largest relative gains suggests that data augmentation particularly benefits models with fewer parameters or less sophisticated feature extraction capabilities.  
Conversely, more recent architectures like InceptionNet-V3 showed smaller but still significant improvements, likely because they already incorporate various forms of architectural regularization that help prevent overfitting. The narrow confidence interval for InceptionNet-V3 also suggests that this architecture produces more consistent results with augmented data.  
EfficientNet-B7's performance is particularly noteworthy, as it achieved both the highest overall accuracy and a substantial improvement from augmentation. This suggests that its efficient scaling of network depth, width, and resolution works synergistically with GAN-augmented data to capture more nuanced features in the images.