Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO
Citations Over TimeTop 10% of 2024 papers
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection methods face problems such as low image resolution and the possibility of missing polyps. In recent years, deep learning techniques have been extensively employed in the detection of colorectal polyps. However, these algorithms have not yet addressed the issue of detection in low-resolution images. In this study, we propose a novel YOLO-SRPD model by integrating SRGAN and YOLO to address the issue of low-resolution colonoscopy images. Firstly, the SRGAN with integrated ACmix is used to convert low-resolution images to high-resolution images. The generated high-resolution images are then used as the training set for polyp detection. Then, the C3_Res2Net is integrated into the YOLOv5 backbone to enhance multiscale feature extraction. Finally, CBAM modules are added before the prediction head to enhance attention to polyp information. The experimental results indicate that YOLO-SRPD achieves a mean average precision (mAP) of 94.2% and a precision of 95.2%. Compared to the original model (YOLOv5), the average accuracy increased by 1.8% and the recall rate increased by 5.6%. These experimental results confirm that YOLO-SRPD can address the low-resolution problem during colorectal polyp detection and exhibit exceptional robustness.
Related Papers
- → Roadside Traffic Sign Detection Based on Faster R-CNN(2019)4 cited
- → Identifying biological terms from text by support vector machine(2011)2 cited
- → An improved algorithm of Chinese comments opinion mining based on adverbs(2016)2 cited
- Application of KNN Algorithm in Automatic Text Categorization(2010)
- → Synergies between low- and high-resolution spectroscopy of exoplanet atmospheres(2022)