CRH-YOLO for precise and efficient detection of gastrointestinal polyps

Gastrointestinal polyps are early indicators of many significant diseases within the digestive system, and timely detection of these polyps is crucial for preventing them. Although clinical gastrointestinal endoscopy and interventions help reduce the risk of malignancy, most current methods fail to adequately address the uncertainties and scale issues associated with the presence of polyps, posing a threat to patients' health. Therefore, this paper proposes a novel single-stage method for polyp detection. Specifically, by designing the CRFEM, the network's ability to perceive contextual information about polyp targets is enhanced. Additionally, the RSPPF is designed to assist the network in more meticulously completing the fusion of multi-scale polyp features. Finally, one detection head is removed from the original model to reduce a substantial number of parameters, and a high-dimensional feature compensation structure is designed to address the decline in recall rate caused by the absence of the detection head. Experiments were conducted using public datasets such as Kvasir-seg, which includes gastric and intestinal polyps. The results indicate that CRH-YOLO achieves 88.8%, 86.0%, and 90.7% on three key metrics: Precision (P), Recall (R), and mean average precision at 0.5 (map@.5), significantly outperforming current mainstream detection models like YOLOv8n. Notably, CRH-YOLO improves the map@.5 metric by 2.4% compared to YOLOv8n. Furthermore, the model demonstrates excellent performance in detecting smaller or less obvious polyps, providing an effective solution for the early detection and prediction of polyps.