journal article·2025Open access

PD-YOLO: Colon Polyp Detection Model Based on Enhanced Small-Target Feature Extraction

Kaixin Lin, Jiajun Hong, Yuanzhi Huang, Yicong Yu, Rong-Guei Tsai

Computers, Materials & Continua· Volume 82· Issue 1

Abstract

In recent years, the number of patients with colon disease has increased significantly. Colon polyps are the precursor lesions of colon cancer. If not diagnosed in time, they can easily develop into colon cancer, posing a ser... | Find, read and cite all the research you need on Tech Science Press
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OpenAlex topics: Radiomics and Machine Learning in Medical Imaging

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References (37)

Show source-provided references
1. Colon polyp detection and segmentation based on improved mrcnn10.1109/TIM.2020.2986875 ↗
2. An interpretable deep neural network for colorectal polyp diagnosis under colonoscopy10.1016/j.knosys.2021.107568 ↗
3. Artificial intelligence-assisted polyp detection for colonoscopy: Initial experience10.1053/j.gastro.2018.04.003 ↗
4. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study10.1136/gutjnl-2018-317500 ↗
5. Accurate classification of diminutive colorectal polyps using computer-aided analysis10.1053/j.gastro.2017.10.010 ↗
6. Automatic detection of colorectal neoplasia in wireless colon capsule endoscopic images using a deep convolutional neural network10.1055/a-1266-1066 ↗
7. A comprehensive study on colorectal polyp segmentation with ResUNet++, conditional random field and test-time augmentation10.1109/JBHI.2021.3049304 ↗
8. FRCNN-AA-CIF: An automatic detection model of colon polyps based on attention awareness and context information fusion10.1016/j.compbiomed.2023.106787 ↗
9. A comparative study on polyp classification using convolutional neural networks10.1371/journal.pone.0236452 ↗
10. A deep convolutional neural network for the detection of polyps in colonoscopy images10.1016/j.bspc.2021.102654 ↗
11. Training data enhancements for improving colonic polyp detection using deep convolutional neural networks10.1016/j.artmed.2020.101988 ↗
12. Z. Chen and S. Lu, “CAF-YOLO: A robust framework for multi-scale lesion detection in biomedical imagery,” 2024, arXiv:2408.01897.
13. Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches10.1016/j.cmpb.2021.106114 ↗
14. Real-time polyp detection model using convolutional neural networks10.1007/s00521-021-06496-4 ↗
15. Super resolution convolutional neural network based pre-processing for automatic polyp detection in colonoscopy images10.1016/j.compeleceng.2020.106959 ↗
16. Comparing UNet, UNet++, FPN, PAN and Deeplabv3+ for gastrointestinal tract disease detection
17. Unet++: A nested u-net architecture for medical image segmentation
18. Feature pyramid networks for object detection
19. L. -C. Chen, “Rethinking atrous convolution for semantic image segmentation,” 2017, arXiv:1706.05587.
20. H. Li, P. Xiong, J. An, and L. Wang, “Pyramid attention network for semantic segmentation,” 2018, arXiv:1805.10180.
21. U-Net: Convolutional networks for biomedical image segmentation
22. Real time detection and localization of colorectal polyps from colonoscopy images: A deep learning approach
23. Knowledge distillation with a class-aware loss for endoscopic disease detection
24. Colonoscopy landmark detection using vision transformers
25. Classification of brain tumor from magnetic resonance imaging using vision transformers ensembling10.3390/curroncol29100590 ↗
26. Multi-classification deep learning models for detection of ulcerative colitis, polyps, and dyed-lifted polyps using wireless capsule endoscopy images10.1007/s40747-023-01271-5 ↗
27. Consolidated domain adaptive detection and localization framework for cross-device colonoscopic images10.1016/j.media.2021.102052 ↗
28. Joint polyp detection and segmentation with heterogeneous endoscopic data
29. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
30. J. Redmon, “YOLOv3: An incremental improvement,” 2018, arXiv:1804.02767.
31. Robust real-time polyp detection system design based on yolo algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC)10.1016/j.eswa.2023.119741 ↗
32. S. Xu et al., “PP-YOLOE: An evolved version of YOLO,” 2022, arXiv:2203.16250.
33. C. -Y. Wang, I. -H. Yeh, and H. -Y. M. Liao, “You only learn one representation: Unified network for multiple tasks,” 2021, arXiv:2105.04206.
34. Detrs beat yolos on real-time object detection
35. C. Lyu et al., “Rtmdet: An empirical study of designing real-time object detectors,” 2022, arXiv:2212.07784.
36. ISA: Ingenious siamese attention for object detection algorithms towards complex scenes10.1016/j.isatra.2023.09.001 ↗
37. Use of artificial intelligence in the detection of the critical view of safety during laparoscopic cholecystectomy10.1016/j.gassur.2024.03.018 ↗