Polyp Size

Polyp size variability poses a significant challenge in automated polyp detection during colonoscopy, impacting the accuracy of diagnostic models. Current research focuses on improving deep learning models, employing architectures like UNets, ConvNextLSTMs, and YOLO-based detectors, often incorporating multi-scale feature fusion and attention mechanisms to address size variations and improve detection of both large and small polyps. These advancements aim to enhance the accuracy and efficiency of polyp detection, ultimately leading to earlier diagnosis and improved outcomes in colorectal cancer prevention. The development of robust and efficient algorithms is crucial for reducing the rate of missed polyps during colonoscopy, a critical step in improving patient care.