Colonoscopy Image

Colonoscopy image analysis focuses on automating the detection and segmentation of polyps, crucial for early colorectal cancer diagnosis. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), transformers, and hybrid architectures to improve segmentation accuracy and generalization across diverse image datasets and polyp characteristics. These advancements aim to reduce human error in polyp detection, leading to earlier diagnosis and improved patient outcomes. Furthermore, research explores depth estimation from 2D colonoscopy images and the use of novel techniques like frequency-based feature fusion and prototype learning to enhance robustness and efficiency.