Research progress on endoscopic image diagnosis of gastric tumors based on deep learning_Journal of Biomedical Engineering

Authors：

GAO Yuan ,  WEI Guohui

College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P. R.China;

Corresponding author：

Keywords：

Deep learning; Gastric tumors; Gastric cancer; Endoscopic images

DOI：

10.7507/1001-5515.202404004

Video：

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Gastric tumors are neoplastic lesions that occur in the stomach, posing a great threat to human health. Gastric cancer represents the malignant form of gastric tumors, and early detection and treatment are crucial for patient recovery. Endoscopic examination is the primary method for diagnosing gastric tumors. Deep learning techniques can automatically extract features from endoscopic images and analyze them, significantly improving the detection rate of gastric cancer and serving as an important tool for auxiliary diagnosis. This paper reviews relevant literature in recent years, presenting the application of deep learning methods in the classification, object detection, and segmentation of gastric tumor endoscopic images. In addition, this paper also summarizes several computer-aided diagnosis (CAD) systems and multimodal algorithms related to gastric tumors, highlights the issues with current deep learning methods, and provides an outlook on future research directions, aiming to promote the clinical application of deep learning methods in the endoscopic diagnosis of gastric tumors.

Citation： GAO Yuan, WEI Guohui. Research progress on endoscopic image diagnosis of gastric tumors based on deep learning. Journal of Biomedical Engineering, 2024, 41(6): 1293-1300. doi: 10.7507/1001-5515.202404004 Copy

1.	Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2.	周迪, 路明亮, 蒋明茗. 胃癌早期诊断的研究. 胃肠病学和肝病学杂志, 2021, 30(6): 606-611.
3.	Choi J, Kim S G, Im J P, et al. Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer. Endoscopy, 2010, 42(9): 705-713.
4.	盖荣丽, 蔡建荣, 王诗宇, 等. 卷积神经网络在图像识别中的应用研究综述. 小型微型计算机系统, 2021, 42(9): 1980-1984.
5.	Sharma P, Hassan C. Artificial intelligence and deep learning for upper gastrointestinal neoplasia. Gastroenterology, 2022, 162(4): 1056-1066.
6.	黄丽冰. 深度学习模型的可解释性及其在医学影像分析应用中的研究进展. 桂林航天工业学院学报, 2023, 28(1): 51-60.
7.	Sakai Y, Takemoto S, Hori K, et al. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network//2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu: IEEE, 2018: 4138-4141.
8.	Li L, Chen Y, Shen Z, et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer, 2020, 23(1): 126-132.
9.	Klang E, Barash Y, Levartovsky A, et al. Differentiation between malignant and benign endoscopic images of gastric ulcers using deep learning. Clinical and Experimental Gastroenterology, 2021, 14: 155-162.
10.	Liu Y, Zhang L, Hao Z, et al. An xception model based on residual attention mechanism for the classification of benign and malignant gastric ulcers. Scientific Reports, 2022, 12(1): 15365.
11.	Wu L, Zhou W, Wan X, et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy, 2019, 51(6): 522-531.
12.	Zhang L, Zhang Y, Wang L, et al. Diagnosis of gastric lesions through a deep convolutional neural network. Digestive Endoscopy, 2021, 33(5): 788-796.
13.	Qiu W, Xie J, Shen Y, et al. Endoscopic image recognition method of gastric cancer based on deep learning model. Expert Systems, 2022, 39(3): e12758.
14.	Chae J W, Cho H C. Enhanced classification of gastric lesions and early gastric cancer diagnosis in gastroscopy using multi-filter autoaugment. IEEE Access, 2023, 11: 29391-29399.
15.	Maashi M, Ali Y, Motwakel A, et al. Anas platyrhynchos optimizer with deep transfer learning based gastric cancer classification on endoscopic images. Electronic Research Archive, 2023, 31(6): 3200-3217.
16.	Durak S, Bayram B, Bakırman T, et al. Deep neural network approaches for detecting gastric polyps in endoscopic images. Medical & Biological Engineering & Computing, 2021, 59(7-8): 1563-1574.
17.	Li X, Chai Y, Zhang K, et al. Early gastric cancer detection based on the combination of convolutional neural network and attention mechanism//2021 China Automation Congress (CAC), Beijing: IEEE, 2021, 10: 5731-5735.
18.	Yao Z, Jin T, Mao B, et al. Construction and multicenter diagnostic verification of intelligent recognition system for endoscopic images from early gastric cancer based on YOLO-V3 algorithm. Frontiers in Oncology, 2022, 12: 815951.
19.	Ahmad S, Kim J S, Park D K, et al. Automated detection of gastric lesions in endoscopic images by leveraging attention-based YOLOv7. IEEE Access, 2023, 11: 87166-87177.
20.	Wu L, Liu J, Yang H, et al. Small gastric polyp detection based on the improved YOLOv5. Multimedia Tools and Applications, 2024, 83(6): 71773-71788.
21.	Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer, 2018, 21(4): 653-660.
22.	Pan D, Zhang R, Wang Y, et al. Detection of early gastric cancer based on single shot detector with feature enhancement//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul: IEEE, 2020: 1091-1095.
23.	Liu M, Luo Z, Cao D, et al. An improved SSD-based gastric cancer detection method//CCF Conference on Computer Supported Cooperative Work and Social Computing, Singapore: Springer Nature Singapore, 2021, 1491: 451-459.
24.	Mushtaq D, Madni T M, Janjua U I, et al. An automatic gastric polyp detection technique using deep learning. International Journal of Imaging Systems and Technology, 2023, 33(3): 866-880.
25.	Mo X, Tao K, Wang Q, et al. An efficient approach for polyps detection in endoscopic videos based on faster R-CNN//2018 24th International Conference on Pattern Recognition (ICPR), Beijing: IEEE, 2018: 3929-3934.
26.	Teramoto A, Shibata T, Yamada H, et al. Automated detection of gastric cancer by retrospective endoscopic image dataset using U-net R-CNN. Applied Sciences, 2021, 11(23): 11275.
27.	Jin J, Zhang Q, Dong B, et al. Automatic detection of early gastric cancer in endoscopy based on mask region-based convolutional neural networks (Mask R-CNN)(with video). Frontiers in Oncology, 2022, 12: 927868.
28.	Cui Z H, Zhang Q Y, Zhang J W, et al. Application of improved mask R-CNN algorithm based on gastroscopic image in detection of early gastric cancer//2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Turin: IEEE, 2022: 1396-1401.
29.	Qiu K, Bu X, Zhou H, et al. Research on ME-NBI gastric lesion recognition system based on improved UNet structure//2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), Xi'an: IEEE, 2022: 482-485.
30.	Du W, Rao N, Yong J, et al. Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet. Computer Methods and Programs in Biomedicine, 2023, 231: 107397.
31.	Zhang Y, Li Y, He D. U-shaped network with multi-scale feature extraction for lesion region segmentation in gastric cancer images//2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing: IEEE, 2023, 3: 234-238.
32.	He D, Zhang Y, Huang H, et al. Dual-branch hybrid network for lesion segmentation in gastric cancer images. Scientific Reports, 2023, 13(1): 6377.
33.	Ling T, Wu L, Fu Y, et al. A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy. Endoscopy, 2021, 53(5): 469-477.
34.	Teramoto A, Shibata T, Yamada H, et al. Detection and characterization of gastric cancer using cascade deep learning model in endoscopic images. Diagnostics, 2022, 12(8): 1996.
35.	Liu L, Dong Z, Cheng J, et al. Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions-a multicenter study. Frontiers in Oncology, 2023, 12: 1075578.
36.	Liang T, Lei Y, Song W, et al. Mask R-CNN with RD Net: a detection and segmentation model for early gastric cancer gastroscopy images//2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2023: 1346-1352.
37.	Zhang K, Wang H, Cheng Y, et al. Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images. Scientific Reports, 2024, 14(1): 7847.
38.	Haq E U, Yong Q, Yuan Z, et al. Accurate multiclassification and segmentation of gastric cancer based on a hybrid cascaded deep learning model with a vision transformer from endoscopic images. Information Sciences, 2024, 670: 120568.
39.	陈伟, 孙康康, 李奇轩, 等. 多模态医学图像融合方法的研究进展. 中国辐射卫生, 2023, 32(5): 580-585.
40.	Ding S, Hu S, Li X, et al. Leveraging multimodal semantic fusion for gastric cancer screening via hierarchical attention mechanism. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(7): 4286-4299.
41.	Lu M, Chai Y. Early gastric cancer screening framework based on multimodal fusion. Lecture Notes in Electrical Engineering Proceedings of 2023 Chinese Intelligent Systems Conference, 2023: 59-67.
42.	Du H, Dong Z, Wu L, et al. A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video). Gastric Cancer, 2023, 26(2): 275-285.
43.	Alrowais F, Alotaibi S, Marzouk R, et al. Manta ray foraging optimization transfer learning-based gastric cancer diagnosis and classification on endoscopic images. Cancers, 2022, 14(22): 5661.
44.	Kim J H, Nam S J, Park S C. Usefulness of artificial intelligence in gastric neoplasms. World Journal of Gastroenterology, 2021, 27(24): 3543-3555.
45.	陈英, 林洪平, 张伟, 等. 医学图像数据集扩充方法研究进展. 生物医学工程学杂志, 2023, 40(1): 185-192.

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2. 周迪, 路明亮, 蒋明茗. 胃癌早期诊断的研究. 胃肠病学和肝病学杂志, 2021, 30(6): 606-611.
3. Choi J, Kim S G, Im J P, et al. Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer. Endoscopy, 2010, 42(9): 705-713.
4. 盖荣丽, 蔡建荣, 王诗宇, 等. 卷积神经网络在图像识别中的应用研究综述. 小型微型计算机系统, 2021, 42(9): 1980-1984.
5. Sharma P, Hassan C. Artificial intelligence and deep learning for upper gastrointestinal neoplasia. Gastroenterology, 2022, 162(4): 1056-1066.
6. 黄丽冰. 深度学习模型的可解释性及其在医学影像分析应用中的研究进展. 桂林航天工业学院学报, 2023, 28(1): 51-60.
7. Sakai Y, Takemoto S, Hori K, et al. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network//2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu: IEEE, 2018: 4138-4141.
8. Li L, Chen Y, Shen Z, et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer, 2020, 23(1): 126-132.
9. Klang E, Barash Y, Levartovsky A, et al. Differentiation between malignant and benign endoscopic images of gastric ulcers using deep learning. Clinical and Experimental Gastroenterology, 2021, 14: 155-162.
10. Liu Y, Zhang L, Hao Z, et al. An xception model based on residual attention mechanism for the classification of benign and malignant gastric ulcers. Scientific Reports, 2022, 12(1): 15365.
11. Wu L, Zhou W, Wan X, et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy, 2019, 51(6): 522-531.
12. Zhang L, Zhang Y, Wang L, et al. Diagnosis of gastric lesions through a deep convolutional neural network. Digestive Endoscopy, 2021, 33(5): 788-796.
13. Qiu W, Xie J, Shen Y, et al. Endoscopic image recognition method of gastric cancer based on deep learning model. Expert Systems, 2022, 39(3): e12758.
14. Chae J W, Cho H C. Enhanced classification of gastric lesions and early gastric cancer diagnosis in gastroscopy using multi-filter autoaugment. IEEE Access, 2023, 11: 29391-29399.
15. Maashi M, Ali Y, Motwakel A, et al. Anas platyrhynchos optimizer with deep transfer learning based gastric cancer classification on endoscopic images. Electronic Research Archive, 2023, 31(6): 3200-3217.
16. Durak S, Bayram B, Bakırman T, et al. Deep neural network approaches for detecting gastric polyps in endoscopic images. Medical & Biological Engineering & Computing, 2021, 59(7-8): 1563-1574.
17. Li X, Chai Y, Zhang K, et al. Early gastric cancer detection based on the combination of convolutional neural network and attention mechanism//2021 China Automation Congress (CAC), Beijing: IEEE, 2021, 10: 5731-5735.
18. Yao Z, Jin T, Mao B, et al. Construction and multicenter diagnostic verification of intelligent recognition system for endoscopic images from early gastric cancer based on YOLO-V3 algorithm. Frontiers in Oncology, 2022, 12: 815951.
19. Ahmad S, Kim J S, Park D K, et al. Automated detection of gastric lesions in endoscopic images by leveraging attention-based YOLOv7. IEEE Access, 2023, 11: 87166-87177.
20. Wu L, Liu J, Yang H, et al. Small gastric polyp detection based on the improved YOLOv5. Multimedia Tools and Applications, 2024, 83(6): 71773-71788.
21. Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer, 2018, 21(4): 653-660.
22. Pan D, Zhang R, Wang Y, et al. Detection of early gastric cancer based on single shot detector with feature enhancement//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul: IEEE, 2020: 1091-1095.
23. Liu M, Luo Z, Cao D, et al. An improved SSD-based gastric cancer detection method//CCF Conference on Computer Supported Cooperative Work and Social Computing, Singapore: Springer Nature Singapore, 2021, 1491: 451-459.
24. Mushtaq D, Madni T M, Janjua U I, et al. An automatic gastric polyp detection technique using deep learning. International Journal of Imaging Systems and Technology, 2023, 33(3): 866-880.
25. Mo X, Tao K, Wang Q, et al. An efficient approach for polyps detection in endoscopic videos based on faster R-CNN//2018 24th International Conference on Pattern Recognition (ICPR), Beijing: IEEE, 2018: 3929-3934.
26. Teramoto A, Shibata T, Yamada H, et al. Automated detection of gastric cancer by retrospective endoscopic image dataset using U-net R-CNN. Applied Sciences, 2021, 11(23): 11275.
27. Jin J, Zhang Q, Dong B, et al. Automatic detection of early gastric cancer in endoscopy based on mask region-based convolutional neural networks (Mask R-CNN)(with video). Frontiers in Oncology, 2022, 12: 927868.
28. Cui Z H, Zhang Q Y, Zhang J W, et al. Application of improved mask R-CNN algorithm based on gastroscopic image in detection of early gastric cancer//2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Turin: IEEE, 2022: 1396-1401.
29. Qiu K, Bu X, Zhou H, et al. Research on ME-NBI gastric lesion recognition system based on improved UNet structure//2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), Xi'an: IEEE, 2022: 482-485.
30. Du W, Rao N, Yong J, et al. Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet. Computer Methods and Programs in Biomedicine, 2023, 231: 107397.
31. Zhang Y, Li Y, He D. U-shaped network with multi-scale feature extraction for lesion region segmentation in gastric cancer images//2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing: IEEE, 2023, 3: 234-238.
32. He D, Zhang Y, Huang H, et al. Dual-branch hybrid network for lesion segmentation in gastric cancer images. Scientific Reports, 2023, 13(1): 6377.
33. Ling T, Wu L, Fu Y, et al. A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy. Endoscopy, 2021, 53(5): 469-477.
34. Teramoto A, Shibata T, Yamada H, et al. Detection and characterization of gastric cancer using cascade deep learning model in endoscopic images. Diagnostics, 2022, 12(8): 1996.
35. Liu L, Dong Z, Cheng J, et al. Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions-a multicenter study. Frontiers in Oncology, 2023, 12: 1075578.
36. Liang T, Lei Y, Song W, et al. Mask R-CNN with RD Net: a detection and segmentation model for early gastric cancer gastroscopy images//2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2023: 1346-1352.
37. Zhang K, Wang H, Cheng Y, et al. Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images. Scientific Reports, 2024, 14(1): 7847.
38. Haq E U, Yong Q, Yuan Z, et al. Accurate multiclassification and segmentation of gastric cancer based on a hybrid cascaded deep learning model with a vision transformer from endoscopic images. Information Sciences, 2024, 670: 120568.
39. 陈伟, 孙康康, 李奇轩, 等. 多模态医学图像融合方法的研究进展. 中国辐射卫生, 2023, 32(5): 580-585.
40. Ding S, Hu S, Li X, et al. Leveraging multimodal semantic fusion for gastric cancer screening via hierarchical attention mechanism. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(7): 4286-4299.
41. Lu M, Chai Y. Early gastric cancer screening framework based on multimodal fusion. Lecture Notes in Electrical Engineering Proceedings of 2023 Chinese Intelligent Systems Conference, 2023: 59-67.
42. Du H, Dong Z, Wu L, et al. A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video). Gastric Cancer, 2023, 26(2): 275-285.
43. Alrowais F, Alotaibi S, Marzouk R, et al. Manta ray foraging optimization transfer learning-based gastric cancer diagnosis and classification on endoscopic images. Cancers, 2022, 14(22): 5661.
44. Kim J H, Nam S J, Park S C. Usefulness of artificial intelligence in gastric neoplasms. World Journal of Gastroenterology, 2021, 27(24): 3543-3555.
45. 陈英, 林洪平, 张伟, 等. 医学图像数据集扩充方法研究进展. 生物医学工程学杂志, 2023, 40(1): 185-192.

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