## 4 Results
### Image Segmentation Results  
This section comprises two primary tasks, namely image mask generation and image segmentation. The dataset contains 1824 images: 612 real colonoscopy images with masks and 300 synthetic images with masks. A pretrained faster R-CNN with ResNet50 backbone first classifies and localizes polyp regions using bounding boxes, which then guide the SAM model for precise mask generation. Image masks were created for shapes identified with the Segment Anything Model (SAM) [49]. The technique starts with initializing SAM with a pre-trained checkpoint and finds circles and outlines in the input image that mimic polyps. Figure 8: IS comparison across three models and three datasets. Figure 7: FID comparison across three models and three datasets. Input points are produced along the boundary of every form found. Figure 9 shows the location of the polyp in both real and AI-generated endoscopic views, highlighting the similarity between actual and synthetic medical imaging data. Following mask generation with the Segment Anything Model (SAM), we performed manual visual screening of all masks generated. This validation process involved systematic examination of each image-mask pair to verify accuracy and alignment with anatomical boundaries. Masks that exhibited inaccuracies or did not properly delineate the target structures were excluded from further analysis, ensuring that the dataset contained only precisely segmented regions for subsequent processing. For model training and evaluation, a standard 80:20 train-test split was applied. This approach ensures robust model development by allocating 80% of the data for training and reserving 20% for independent testing, allowing for a comprehensive assessment of the performance and generalizability of the segmentation models. The analyses of the five segmentation models showed that the metrics had varying effects on each model's performance. With the highest IoU (0.65), UNet showed strong performance in many metrics, particularly achieving the highest recall (0.85) and Dice coefficient (0.78). FPN demonstrated solid results with the highest precision (0.77) and PSNR (7.21). LinkNet achieved the highest F1 score (0.78) among all models, although its other metrics were comparable to its peers. PSPNet showed slightly lower overall performance, with the lowest IoU (0.63) and PSNR (6.64). MANet's performance was middle of the pack across most metrics, with values generally similar to FPN and LinkNet. Regarding structural similarity, all models showed relatively low SSIM scores ranging from 0.44 to 0.49, with UNet and FPN tied for the highest at 0.49. The PSNR values were consistently in the range of 6-7 in all models, with the FPN achieving the highest at 7.21. Figures 10 and 11 show the segmentation findings for real and synthetic colonoscopy image polyp images. A pink growth is shown against a crimson intestinal lining in the original photograph. The real mask displays the polyp area as white. The segmentation of the UNet is shown in black on the expected mask. Green indicates a valid identification, red indicates a missing area, and yellow indicates a false positive. The overlay contrasts the results. UNet accurately depicts the polyp's form and position but overestimates its size, especially around the edges. UNet's multiscale feature recognition and over-segmentation are shown  
Figure 9: Comparison of real and synthetic colonoscopy images with polyp detection. Top row shows a real colonoscopy image with its corresponding binary mask and region overlay. Bottom row displays a synthetic colonoscopy image with its binary mask and region overlay. here. Perhaps fine-tuning or postprocessing could sharpen edges. The results of this study are shown in Table 3. Table 3 shows the Dice coefficients.