## 4 Results
### Image Classification Results  
The dataset used for classification consists of three sources: the original image dataset contains 1184 test images, 4138 training images, and 592 validation images; the synthetic image dataset has 358 test images, 1260 training images, and 182 validation images; and the augmented dataset comprises 1542 test images, 5398 training images, and 774 validation images. Advanced classification models, including ViT, BiT, FixResNeXt, DeiT, and EfficientNet, were trained using images generated from this investigation. Conventional metrics were used to evaluate these models. This comprehensive strategy aims to improve medical diagnosis and imaging technology. Figure 12 illustrates a 5 \(\times\) 2 grid of original colonoscopy data randomly selected. Figure 13 illustrates the results of the models based on the original data. EfficientNet has a pronounced superiority in all performance criteria used. Each metric confirmed that EfficientNet has one of the highest validation accuracy values of 97% and an F1 measures of 96.79% and is superior to all the models used. FixResNeXt was slightly less successful with a maximum validation accuracy of 86% and an F1 score of 85.95%. ViT's results are the second best of all networks, although they are far less impressive than EfficientNet's 96.79% AUC ratings. Loss measurements shed more light on this discrepancy in performance: FixResNeXt has a validation loss of 0.36, while the remaining models have even higher levels of loss. The validation loss of EfficientNet is 0.11, making it the best performing model. Precision and recall also retain the same traits and are presented as follows. Validation precision and recall of EfficientNet: 96.8%, 96.8%; validation of the second best model, FixResNeXt: 86.2%, 85.9%. Specifically, most of the models demonstrate low discrepancies between the training and validation performance. EfficientNet demonstrates better adaptability to the data of this set, as well as higher efficiency in terms of all scores. The large accuracy margin points to the fact that the EfficientNet architecture is learning features and patterns in the  
Figure 12: Endoscopic views from a colonoscopy procedure showing various segments and mucosal patterns of the colon interior. data that other architectures are unable to, thereby making EfficientNet suitable for tasks such as the ones used in this initial dataset. Figure 14 shows a 5 \(\times\) 2 grid of randomly sampled synthetic colonoscopy data. Looking at the metrics in Figure 15, EfficientNet is smoother in almost all metrics, although not as dominant as it was with the initial data. The percentage accuracy of the training was 80 %, the accuracy of the validation was 79 %, and the F1 scores for the training and validation were 79.9% and 79.1%, respectively, which are comparatively lower than the scores obtained on the original data. Other models demonstrate better relative performance, specifically based on the metrics from the validation sets. FixResNeXt takes second place with the best validation accuracy of 71%, and an F1 score of 70.8%. BiT achieved a fantastic validation precision of 75.64% and ViT achieved a validation precision of 75.47%, which is higher than the training precisions of 64.98% and 61.46%, respectively. Loss metrics further illustrate the narrowed gap: FixResNeXt's 0.58 loss and other models' losses are much closer to the validation loss of 0.5133, which belongs to EfficientNet. Figure 14: Synthetic endoscopic images generated to simulate various views and conditions encountered during medical endoscopy procedures. Figure 13: Performance comparison of deep learning and transformer-based models across multiple evaluation metrics, showing EfficientNet achieving superior performance across all measures on the original image dataset. An evaluation of overall performance highlights the fact that, with most models obtaining percentage scores within the range of 60-70% for the different evaluation criteria, synthetic data seem more difficult.