## 4 Results
### Image Classification Results
This results from heightened complexity, variety, and noise in the synthetic data production process. The results indicate that the performance discrepancies between the models and the synthetic data stem from possible deficiencies not present in the actual data. Synthetic data may effectively enhance the evaluation of a model's robustness and its ability to generalize to novel occurrences. Figure 16 shows a 5 \(\times\) 5 grid of randomly sampled augmented colonoscopy data (combination of original and synthetic data). The augmented dataset causes a significant reversal in the performance of the various models and shows the variation in the data characteristics that affect the model performance. In this case, DeiT and ViT are the best that demonstrate high accuracy and F1 score, as well as the lowest error rate. Training precision of 98% was achieved, with a validation accuracy of 93%, while F1 scores achieved 97.94% in training and 92.89% in validation. The DeiT has shown the following performance, which shows a great improvement compared to the previous models. ViT achieved 93% validation accuracy and a 92.51% F1 score; in contrast, BiT achieved 92% validation accuracy with a 92.38% F1 score. This shows that the augmented dataset is well suited for transformer-based architectures. Figure 16: Combined dataset of real and synthetically generated endoscopic images, demonstrating the visual similarity between actual endoscopic findings and artificially generated medical imaging data. Figure 15: Performance evaluation of five deep learning and transformer-based models on AI-generated colonoscopy data, demonstrating generally lower but more consistent performance across models compared to original data, with EfficientNet maintaining superior accuracy but showing more metric variation. In contrast, EfficientNet outperformed all other models in the previous datasets but has the lowest validation in this case, which is a precision score 86%. This change in the improvement rate is stark and underlines the importance of specific dataset attributes for model effectiveness. Loss metrics also point to these performance changes. Figure 17 shows that DeiT validation loss is 0.20, which is less than Efficient Net's 0.38 and the opposite of what has been seen in previous datasets. Relative performance can also be seen in the AUC scores, where DeiT achieved 92.90%, while EfficientNet achieved 86.05%. One unique feature of the intelligent data results is that most models had validation scores greater than 90% (EfficientNet not included). Training and validation performance are also closely matched for the top models with relatively little gap. For example, the training accuracy of 98% compared to the validation accuracy of 93% shows that DeiT and ViT have great generalization as seen in Figure 17.  
These results indicate that the intelligent dataset contains distinguishable patterns, which makes transformer-based models highly effective. The high overall scores, as well as the proximity to the training validation performance, indicate that this dataset is more 'teachable' in gross terms, but with certain attributes that tend to benefit a given architectural design. This scenario shows that the structure of the model should correspond to the type of data and the qualitative difference that occurs in the work of the model in different sets. This research captures major variations in the performance of the various models. Looking at the performance of the models in the original dataset, EfficientNet has the highest accuracy score and the highest F1 score (97% accuracy, 96.79% F1). FixResNeXt comes next in a short time, followed by other models that range from at most 75-80% on most of the metrics. In the case of the synthetic dataset, overall model performances were comparatively lower than those achieved on the original dataset. EfficientNet retains a sample advantage over FixExNet, although the score boundaries are significantly lower (79% validation accuracy).