## 2 Related Work
The models appear to be resilient to variations in appearance, tiny objects, and rotation, demonstrating strong performance on hitherto unseen data. The limits of polyps are difficult to see due to overlapping light and shadow, as well as numerous false positives from the reflection point. The model surpasses polyp segmentation techniques in recommended colonoscopy applications. [PERSON] et al. [35] introduced Dilated ResFCN and SE-Unet, two innovative fully convolutional neural network (FCNN) architectures for the segmentation of colonoscopy polyps. The average Dice score was 0.8343, the standard deviation was 0.1837, and only three polyps were overlooked. The strategies mentioned above secured victories in the Gastrointestinal Image ANAlysis (GINA) tournaments in 2017 and 2018. The study was beneficial; however, the temporal dependencies of colonoscopy records should enhance future research. [PERSON] et al. [18] used Mask R-CNN to identify and segment colonoscopy polyps to reduce the physician 25% missed rate. Multiple CNN feature extractors and additional training data were evaluated to construct Mask R-CNN. An ensemble method for enhancement was proposed. The segmentation of the leading model was performed on the 2015 Medical Image Computing and Computer Assisted Intervention (MICCAI) dataset, with the following results: recall 72.59%, accuracy 80%, Dice coefficient 70.42%, and Jaccard index 61.24%. The study elucidates the trade-offs between model complexity and dataset quality for better polyp identification and segmentation. The results are promising, but automated polyp recognition should be improved. [PERSON] et al. [36] tested CNN architectures for gastrointestinal endoscopy using 110,079 images and 374 movies for multiclass classification. Size, diversity, segmentation masks, and bounding boxes help, but class imbalance and interobserver variability hurt. ResNet-152 and DenseNet-161 achieved amazing results with microaveraged F1 scores of 0.910, macroaverages of 0.617, and Matthews Correlation Coefficients of 0.902. Despite its appeal, the model struggled to distinguish esophagitis from ulcerative colitis. The material is suitable for AI-assisted gastrointestinal endoscopic diagnostic tools; however, classification accuracy needs to be improved. [PERSON] et al. [21] used a large training dataset to test EndoBRAIN, an AI system that detects colorectal polyps from endocytoscopic images. The model outperformed the trainee and professional endoscopists in stained image analysis with 96.9% sensitivity, 100% specificity, and 98% precision. Size and various imaging modalities are benefits, but retrospective design and Japanese center concentration are weaknesses. EndoBRAIN's 100% positive and 94.6% negative predictive scores improve colorectal polyp diagnosis. More prospective trials are needed to prove that AI can improve endoscopic diagnostics. [PERSON] et al. [22] developed ENDOANGEL, a deep convolutional neural network-based method to assess the quality of intestinal preparation through precise image and video analysis. This model surpassed human endoscopists, achieving 93.33% accuracy on conventional images and 80.00% accuracy on bubble images. The video examination demonstrated an accuracy of 89.04%. The benefits of ENDOANGEL include an objective and consistent evaluation, immediate colonoscopy scoring, and enhanced image and video quality. The study had limitations, including the use of retrospective data collection for training and restricted video assessment. The reproducible evaluation of intestinal preparation as a continuous variable during colonoscopy withdrawal is an innovative approach that enhances the potential to improve colonoscopy results and standardize the evaluation of stool preparation quality. The literature review shows that AI for colonoscopy analysis, particularly polyp detection and categorization, has advanced. However, significant gaps and constraints remain due to the bias of the models resulting from the reduced variability of the training dataset [3, 25].