Real-time detection of colon polyps during colonoscopy using YOLOv7

Abstract

Deep learning has made brilliant achievements in detecting colonic polyps in colonoscopy videos in recent years. However, the detection of colonic polyps in colonoscopy videos is problematic because of the complex environment of the colon and the various shapes of polyps. Therefore, researchers need to spend a lot of time searching for real-time detection systems with good performance and that are suitable for the current equipment and working environment. This paper aimed to investigate the polyp detection potential of the state-of-the-art deep learning model You Only Look Once version 7. We implemented, trained, and tested the polyp detection model using open public datasets: Kvasir-Seg, CVC-ClinicDB, CVC_ColonDB, and ETIS-LaribPolypDB. Validation of the test set utilizing Recall, Precision, F1 Score, and Average Precision (AP) showed that the model achieved the highest performance on CVC-ClinicDB with 83.3% Recall, 80.6% Precision, 81.9% F1 Score, 75% AP@0.5, 51.8% AP and the mean processing time per frame was 20ms. The automatic polyp detection model exhibited good performance, as evidenced by the high detection sensitivity and rapid processing. This model can help endoscopists improve polyp detection performance during the colonoscopy procedure.