## 2 Related Work
They analyzed numerous advanced strategies and tackled the need for dynamic generation of medical images from textual descriptions. The models were evaluated using single-center and multicenter datasets. SD beat other Frechet Inception Distance techniques. The highest multicenter score of 0.064 indicated excellent image quality. DB + LoRA outperformed CLIP (1.568) and SD (2.362) in the initial score. SD produced diversified and high-quality images efficiently, beating FID and competing in IS. Their analysis did not evaluate the images generated clinically or diagnostically. The research shows the potential of AI-generated medical imaging, but it also stresses the need for clinical trials and ethical considerations. [PERSON] et al. [30] explored how deep learning has improved colonoscopy polyp identification. The CNN is the most popular architecture for end-to-end applications; hybrid methods are less popular. The biggest issues are large, annotated datasets and inconsistent evaluation metrics. Their research shows that these methods can improve the identification of adenoma but require clinical validation. Standardization of assessment methods, semisupervised learning, elimination of Histogram of Displacement (HD) colonoscopy images, and improved access to large public datasets can result. [PERSON] [24] conducted an extensive evaluation of data enhancement methodologies for medical imaging. The efficacy of augmentation techniques varies according to the specific medical imaging modality used. Shearing and translation proved to be most effective for brain Magnetic Resonance Imaging (MRI), lung Computed Tomography (CT), and breast mammography, but noise-based methods often faltered. The most precise components of brain MRI were translation, shearing, and rotation, with an accuracy of 88.24%. Translation by shearing exhibited optimal performance on lung CT at 85.74%. Translation, shearing, and rotation were the most beneficial for breast mammography (83.34%). The results for color shift, sharpening, and contrast modification were most pronounced in the classification of the eye fundus, with a precision of 88.24%. Shearing and noise performed poorly, but the combinations performed better. Fundus images had the greatest improvement in color and contrast. GAN-based methods are more diverse, yet suffer from gradient vanishing [31; 32]. The assessment stresses image categorization and the selection of task-related augmentation techniques. [PERSON] et al. [33] used deep learning in real time to identify colonoscopy polyps. In addition to 27,113 images and videos, 1290 patient records confirmed findings. The approach performed well in datasets, with per-image sensitivity from 88.24% to 94.38% and specificity from 95.40% to 95.92%. Please note that the confirmed polyp videos were 100% sensitive. The gadget can handle 25 frames per second with low latency for real-time clinical applications. This method allows endoscopists to test their polyp-detecting skills. This pilot study by [PERSON] et al. [34] evaluates a computer-aided detection (CADe) system for the real-time identification of polyps during colonoscopy. Although previous CADe systems were capable of recognizing over 90% static images and 70% videos, the extract failed to include the performance characteristics of the system. Expanding datasets and including video-based analysis could improve the practical importance of this research. The absence of performance metrics in the passage is concerning. The initiative aims to reduce human error in polyp detection, thus improving adenoma diagnosis and perhaps decreasing the rates of colorectal cancer in intervals. [PERSON] et al. [19] used Polyp-olpy Pyramid Vision Transformer(PVT) and a transformer encoder that incorporates Context Feature Module (CFM), Cross-interaction Module (CIM), and SAM segment polyps, achieving a score of 0.900 Dice and 0.833 IoU on the Endoscene dataset, surpassing previous approaches in five datasets.