## 5 Discussion  
This study created a novel strategy to address the lack of medical imaging data, especially when it comes to colonoscopy operations for underrepresented groups. The main objective of the study was to improve the detection models of CRC by combining various data augmentation approaches with sophisticated machine learning and deep learning techniques to synthesize colonoscopy images. To produce realistic artificial colonoscopy images, the researchers modified and improved several visual generative AI models, such as CLIP, Stable Diffusion (SD), and DreamBooth (DB) with LoRA. The Frechet Inception Distance and Inception score were used to thoroughly assess the quality of these produced images. The research used several models for image classification tasks, including EfficientNet, FixResNeXt, Big Transfer, Vision Transformer, and Data-efficient Image Transformers. The study used original, augmented, and synthetic datasets for testing and training. And also for image segmentation tasks, the study implemented U-Net, PSNR, FPN, LinkNet, and MANet. With lower FID scores across all datasets, the results showed that DreamBooth in conjunction with LoRA created the most realistic photos. ViT and DeiT models achieved 93% validation precision and 92.89% F1 scores in classification tasks, according to the expanded dataset. This showed that creating synthetic images from high-quality data can improve model performance and generalizability across various demographic subgroups. The CLIP, SD, and DB LoRa models were all successfully used for colonoscopy image generation in the study. Fine-tuned SD and DB LoRa produced the lowest FID scores for the datasets in terms of image originality and quality. The adapted models achieved optimal results when generating several images of synthetic colonoscopy. Inception scores were used to prove the diversity and realism of the generated image. DB LoRa received the highest average IS value of 2.36 for all datasets, indicating good image variety and quality. The quality and clinical usefulness of the images generated have therefore been quantitatively assessed using FID and IS metrics. The FID scores remained below 15 while the IS values were above 2 in all different datasets, which is an indication of the high quality and diversification of the images synthesized. The researchers were able to train and test the ViT, BiT, FixResNeXt, DeiT, and EfficientNet models using synthetic, original, and augmented data. The augmented data strengthened the results in addition to obtaining a higher validation precision with ViT and DeiT both having a precision of 93% and an F1 score of 92.89%, which means that synthetic data enhancement is effective. For the image segmentation task, the study adopted and assessed the performance of U-Net, PSNR, FPN, LinkNet, and MANet. FPN produced excellent results, with an IoU of 0.64, an F1 score of 0.78, a recall of 0.75, and a Dice coefficient of 0.77. This study clearly showed both improved performance and fairness of the model with augmented synthetic data. The augmented dataset performed steadily better than the original and synthetic datasets for all models and measures, indicating lower bias and better generalization. All research objectives were met systematically, indicating general research contributions in the areas of synthetic image generation, improved model performance, and possible elimination of bias when using AI-enabled analysis of colonoscopy images. By improving AI-based colonoscopy screening with improved training datasets, this study significantly reduces health disparities in CRC therapy outcomes. In addition to demonstrating how artificial data augmentation can improve the effectiveness and equity of AI models for polyp recognition and classification, this study advances the field of generative AI in medical imaging. The innovative approach to medical picture synthesis, extensive evaluation techniques, improved model performance, and important implications for healthcare equity are just a few of its strengths.