Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network_Journal of Biomedical Engineering

Authors：

LI Yiyang ¹ , ZHAO Jiayi ² , YU Ruoyi ² , LIU Huixiang ^3,4 ,  LIANG Shuang ^1,4,5 ,  GU Yu ^1,4,5

1. School of Biomedical Engineering, Capital Medical University, Beijing 100069, P. R. China;
2. School of Basic Medical Sciences, Capital Medical University, Beijing 100069, P. R. China;
3. School of Automation, Beijing Information Science and Technology University, Beijing 100192, P. R. China;
4. Beijing Key Laboratory of Fundamicationental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing 100069, P. R. China;
5. Laboratory for Clinical Medicine, Capital Medical University, Beijing 100069, P. R. China;

Corresponding?author：

LIANG Shuang, Email: shliang@ccmu.edu.cn; GU Yu, Email: ygu@ccmu.edu.cn

Keywords：

Colorectal polyps; Colorectal cancer; Target detection; Computer-aided diagnosis; Deep learning

DOI：

10.7507/1001-5515.202312014

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.

Citation： LI Yiyang, ZHAO Jiayi, YU Ruoyi, LIU Huixiang, LIANG Shuang, GU Yu. Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network. Journal of Biomedical Engineering, 2024, 41(5): 911-918. doi: 10.7507/1001-5515.202312014 Copy

1.	周雄, 胡明, 李子帥, 等. 2020年全球及中國結直腸癌流行狀況分析. 海軍軍醫大學學報, 2022, 43(12): 1356-1364.
2.	田曉彤, 卜淑蕊. 結直腸息肉的臨床特征研究. 臨床內科雜志, 2022, 39(2): 91-94.
3.	禹蓉, 董衛國, 田山, 等. 不同病理類型結直腸息肉癌變的臨床研究進展. 中國全科醫學, 2023, 26(14): 1790-1794.
4.	姜華, 曹金恒. 結腸鏡下結直腸息肉分型與治療的研究進展. 內蒙古醫學雜志, 2022, 54(9): 1060-1064.
5.	王成龍, 趙子夜, 王顥, 等. 結腸鏡檢查質量控制研究進展. 結直腸肛門外科, 2021, 27(6): 527-530,535.
6.	Corley D A, Jensen C D, Marks A R, et al. Adenoma detection rate and risk of colorectal cancer and death. New England Journal of Medicine, 2014, 370(14): 1298-1306.
7.	林燕鳳, 趙舷宏, 付朝麗, 等. 以人工智能腸道圖像識別模型評估結腸鏡檢查前腸道準備. 中國醫學影像術, 2023, 39(7): 1034-1038.
8.	Barclay R L, Vicari J J, Greenlaw R L. Effect of a time-dependent colonoscopic withdrawal protocol on adenoma detection during screening colonoscopy. Clinical Gastroenterology and Hepatology, 2008, 6(10): 1091-1098.
9.	王樹玲, 康爭春, 趙勝兵, 等. 加強結腸鏡檢查規范培訓,提高結腸腺瘤檢出率. 海軍軍醫大學學報, 2023, 44(3): 265-271.
10.	馮燕, 高峰. 結直腸息肉內鏡檢出率影響因素分析. 新疆醫學, 2017, 47(11): 1313-1317.
11.	Bretthauer M, Kaminski MF, L?berg M, et al. Population-based colonoscopy screening for colorectal cancer: a randomized clinical trial. JAMA Intern Med, 2016, 176(7): 894-902.
12.	熊逢春, 吳小微, 王計. 人工智能在結直腸息肉檢測與診斷中的應用. 現代消化及介入診療, 2022, 27(6): 663-667.
13.	李小雷. 人工智能在醫學影像圖像處理中的研究進展. 中國醫學計算機成像雜志, 2023, 29(4): 454-457.
14.	Byrne M F, Shahidi N, Rex D K. Will computer-aided detection and diagnosis revolutionize colonoscopy?. Gastroenterology, 2017, 153(6): 1460-1464.
15.	木合塔爾·色特瓦勒迪, 古麗巴哈爾·司馬義. 人工智能在診斷結直腸癌癌前病變中的應用. 胃腸病學和肝病學雜志, 2023, 32(5): 584-587.
16.	王麗梅, 馮璜, 陳衛昌, 等. 基于人工智能的結直腸息肉輔助診斷系統可提高息肉檢出率: 一項前瞻性隨機對照研究. 胃腸病學, 2022, 27(3): 163-167.
17.	Sánchez-Peralta L F, Bote-Curiel L, Picón A, et al. Deep learning to find colorectal polyps in colonoscopy: a systematic literature review. Artificial Intelligence in Medicine, 2020, 108: 101923.
18.	劉濤, 丁雪妍, 張冰冰, 等. 改進YOLOv5的遙感圖像檢測方法. 計算機工程與應用, 2023, 59(10): 253-261.
19.	Sornapudi S, Meng F, Yi S. Region-based automated localization of colonoscopy and wireless capsule endoscopy polyps. Applied Sciences. 2019, 9(12): 2404.
20.	考文濤, 李明, 馬金剛. 卷積神經網絡在結直腸息肉輔助診斷中的應用綜述. 計算機科學與探索, 2024, 18(3): 627-645.
21.	Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: IEEE, 2016: 779-788.
22.	Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector//Computer Vision – ECCV 2016, Amsterdam: Springer International Publishing, 2016: 21–37.
23.	朱豪, 周順勇, 劉學, 等. 基于深度學習的單階段目標檢測算法綜述. 工業控制計算機, 2023, 36(4): 101-103.
24.	邱天衡, 王玲, 王鵬, 等. 基于改進YOLOv5的目標檢測算法研究. 計算機工程與應用, 2022, 58(13): 63-73.
25.	梁禮明, 何安軍, 李仁杰, 等. 跨尺度跨維度的自適應Transformer網絡應用于結直腸息肉分割. 光學精密工程, 2023, 31(18): 2700-2712.
26.	徐昌佳, 易見兵, 曹鋒, 等. 采用DoubleUNet網絡的結直腸息肉分割算法. 光學精密工程, 2022, 30(8): 970-983.
27.	Wan J, Chen B, Yu Y. Polyp detection from colorectum images by using attentive YOLOv5. Diagnostics, 2021, 11(12): 2264.
28.	司丙奇, 王志武, 姜萍萍, 等. 基于改進 YOLOv5 網絡的內窺鏡息肉檢測. 中國新通信, 2022, 24(11): 123-128.
29.	Wittenberg T, Zobel P, Rathke M, et al. Computeraided detection of polyps in whitelight- colonoscopy images using deep neural networks. Current Directions in Biomedical Engineering, 2019, 5(1): 231-234.
30.	Liu M, Jiang J, Wang Z. Colonic polyp detection in endoscopic videos with single shot detection based deep convolutional neural network. IEEE Access, 2019, 7: 75058-75066.

1. 周雄, 胡明, 李子帥, 等. 2020年全球及中國結直腸癌流行狀況分析. 海軍軍醫大學學報, 2022, 43(12): 1356-1364.
2. 田曉彤, 卜淑蕊. 結直腸息肉的臨床特征研究. 臨床內科雜志, 2022, 39(2): 91-94.
3. 禹蓉, 董衛國, 田山, 等. 不同病理類型結直腸息肉癌變的臨床研究進展. 中國全科醫學, 2023, 26(14): 1790-1794.
4. 姜華, 曹金恒. 結腸鏡下結直腸息肉分型與治療的研究進展. 內蒙古醫學雜志, 2022, 54(9): 1060-1064.
5. 王成龍, 趙子夜, 王顥, 等. 結腸鏡檢查質量控制研究進展. 結直腸肛門外科, 2021, 27(6): 527-530,535.
6. Corley D A, Jensen C D, Marks A R, et al. Adenoma detection rate and risk of colorectal cancer and death. New England Journal of Medicine, 2014, 370(14): 1298-1306.
7. 林燕鳳, 趙舷宏, 付朝麗, 等. 以人工智能腸道圖像識別模型評估結腸鏡檢查前腸道準備. 中國醫學影像術, 2023, 39(7): 1034-1038.
8. Barclay R L, Vicari J J, Greenlaw R L. Effect of a time-dependent colonoscopic withdrawal protocol on adenoma detection during screening colonoscopy. Clinical Gastroenterology and Hepatology, 2008, 6(10): 1091-1098.
9. 王樹玲, 康爭春, 趙勝兵, 等. 加強結腸鏡檢查規范培訓,提高結腸腺瘤檢出率. 海軍軍醫大學學報, 2023, 44(3): 265-271.
10. 馮燕, 高峰. 結直腸息肉內鏡檢出率影響因素分析. 新疆醫學, 2017, 47(11): 1313-1317.
11. Bretthauer M, Kaminski MF, L?berg M, et al. Population-based colonoscopy screening for colorectal cancer: a randomized clinical trial. JAMA Intern Med, 2016, 176(7): 894-902.
12. 熊逢春, 吳小微, 王計. 人工智能在結直腸息肉檢測與診斷中的應用. 現代消化及介入診療, 2022, 27(6): 663-667.
13. 李小雷. 人工智能在醫學影像圖像處理中的研究進展. 中國醫學計算機成像雜志, 2023, 29(4): 454-457.
14. Byrne M F, Shahidi N, Rex D K. Will computer-aided detection and diagnosis revolutionize colonoscopy?. Gastroenterology, 2017, 153(6): 1460-1464.
15. 木合塔爾·色特瓦勒迪, 古麗巴哈爾·司馬義. 人工智能在診斷結直腸癌癌前病變中的應用. 胃腸病學和肝病學雜志, 2023, 32(5): 584-587.
16. 王麗梅, 馮璜, 陳衛昌, 等. 基于人工智能的結直腸息肉輔助診斷系統可提高息肉檢出率: 一項前瞻性隨機對照研究. 胃腸病學, 2022, 27(3): 163-167.
17. Sánchez-Peralta L F, Bote-Curiel L, Picón A, et al. Deep learning to find colorectal polyps in colonoscopy: a systematic literature review. Artificial Intelligence in Medicine, 2020, 108: 101923.
18. 劉濤, 丁雪妍, 張冰冰, 等. 改進YOLOv5的遙感圖像檢測方法. 計算機工程與應用, 2023, 59(10): 253-261.
19. Sornapudi S, Meng F, Yi S. Region-based automated localization of colonoscopy and wireless capsule endoscopy polyps. Applied Sciences. 2019, 9(12): 2404.
20. 考文濤, 李明, 馬金剛. 卷積神經網絡在結直腸息肉輔助診斷中的應用綜述. 計算機科學與探索, 2024, 18(3): 627-645.
21. Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: IEEE, 2016: 779-788.
22. Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector//Computer Vision – ECCV 2016, Amsterdam: Springer International Publishing, 2016: 21–37.
23. 朱豪, 周順勇, 劉學, 等. 基于深度學習的單階段目標檢測算法綜述. 工業控制計算機, 2023, 36(4): 101-103.
24. 邱天衡, 王玲, 王鵬, 等. 基于改進YOLOv5的目標檢測算法研究. 計算機工程與應用, 2022, 58(13): 63-73.
25. 梁禮明, 何安軍, 李仁杰, 等. 跨尺度跨維度的自適應Transformer網絡應用于結直腸息肉分割. 光學精密工程, 2023, 31(18): 2700-2712.
26. 徐昌佳, 易見兵, 曹鋒, 等. 采用DoubleUNet網絡的結直腸息肉分割算法. 光學精密工程, 2022, 30(8): 970-983.
27. Wan J, Chen B, Yu Y. Polyp detection from colorectum images by using attentive YOLOv5. Diagnostics, 2021, 11(12): 2264.
28. 司丙奇, 王志武, 姜萍萍, 等. 基于改進 YOLOv5 網絡的內窺鏡息肉檢測. 中國新通信, 2022, 24(11): 123-128.
29. Wittenberg T, Zobel P, Rathke M, et al. Computeraided detection of polyps in whitelight- colonoscopy images using deep neural networks. Current Directions in Biomedical Engineering, 2019, 5(1): 231-234.
30. Liu M, Jiang J, Wang Z. Colonic polyp detection in endoscopic videos with single shot detection based deep convolutional neural network. IEEE Access, 2019, 7: 75058-75066.

Journal of Biomedical Engineering

Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content