Browse > Article
http://dx.doi.org/10.9708/jksci.2021.26.03.019

Automatic Anatomical Classification Model of Esophagogastroduodenoscopy Images Using Deep Convolutional Neural Networks for Guiding Endoscopic Photodocumentation  

Park, Jung-Whan (Dept. of Computer Science and Engineering, Kangwon National University)
Kim, Yoon (Dept. of Computer Science and Engineering, Kangwon National University)
Kim, Woo-Jin (Dept. of Internal Medicine and Biomedical Informatics, Kangwon National University)
Nam, Seung-Joo (Dept. of Internal Medicine, Kangwon National University School of Medicine)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.3, 2021 , pp. 19-28 More about this Journal
Abstract
Esophagogastroduodenoscopy is a method commonly used for early diagnosis of upper gastrointestinal lesions. However, 10-20 percent of the gastric lesions are reported to be missed, due to human error. And countries including the US, the UK, and Japan, the World Endoscopy Organization (WEO) suggested guidelines about essential gastrointestinal parts to take pictures of so that all gastric lesions are observed. In this paper, we propose deep learning techniques for classification of anatomical sites, aiming for the system that informs practitioners whether they successfully did the gastroscopy without blind spots. The proposed model uses pre-processing modules and data augmentation techniques suitable for gastroscopy images. Not only does the experiment result with a maximum F1 score of 99.6%, but it also shows a error rate of less than 4% based on the actual data. Given the performance results, we found the model to be explainable with the potential to be utilized in the clinical area.
Keywords
Deep Learning; Medical Image Analysis; EsophagoGastroDuodenoscopy; Stomach Anatomy Site Classification; Image Processing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Misawa, Masashi, Shin-ei Kudo, Yuichi Mori, Hiroki Nakamura, Shinichi Kataoka, Yasuharu Maeda, Toyoki Kudo et al. "Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy." Gastroenterology VOLUME 150, ISSUE 7, pp 1531-1532, JUNE 01, 2016, DOI: https://doi.org/10.1053/j.gastro.2016.04.004   DOI
2 Urban, Gregor, et al. "Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy." Gastroenterology Volume 155, Issue 4, pp 1069-1078, October 2018, DOI: https://doi.org/10.1053/j.gastro.2018.06.037   DOI
3 Lui, Thomas KL, Vivien WM Tsui, and Wai K. Leung. "Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis." Gastrointestinal endoscopy Volume 92, Issue 4, pp 821-830. October 2020, DOI: https://doi.org/10.1016/j.gie.2020.06.034   DOI
4 Luo, Huiyan, et al. "Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study." The Lancet Oncology 20.12 pp 1645-1654, Dec 2019 DOI: https://doi.org/10.1016/S1470-2045(19)30637-0   DOI
5 Chadwick G, Groene O, Riley S, et al. "Gastric Cancers Missed During Endoscopy in England". Clin Gastroenterol Hepato l13, no. 7 pp: 1264-1270, Jul 2015 DOI: https://doi.org/10.1016/j.cgh.2015.01.025   DOI
6 Wang YR, Loftus EV, Jr., Judge TA, Peikin SR. "Rate and Predictors of Interval Esophageal and Gastric Cancers after Esophagogastroduodenoscopy in the United States. Digestion", Digestion 94, no. 3, pp 176-180, 2016 DOI: https://doi.org/10.1159/000452794   DOI
7 Yalamarthi S, Witherspoon P, McCole D, Auld CD. "Missed diagnoses in patients with upper gastrointestinal cancers. Endoscopy", Endoscopy 36, no. 10, pp 874-879, Sep 2004 DOI:https://doi.org/10.1055/s-2004-825853   DOI
8 Yao, Kenshi. "The endoscopic diagnosis of early gastric cancer." Annals of Gastroenterology: Quarterly Publication of the Hellenic Society of Gastroenterology 26.1 p 11, 2013:
9 Bisschops, Raf, et al. "Performance measures for upper gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) quality improvement initiative." endoscopy 48, no. 09 pp 843-864. Sep 2016 DOI http://dx.doi.org/10.1055/s-0042-113128   DOI
10 Emura, Fabian, et al. "Principles and practice to facilitate complete photodocumentation of the upper gastrointestinal tract: World Endoscopy Organization position statement." Digestive Endoscopy 32, no. 2 pp 168-179, Jan 2020 DOI: https://doi.org/10.1111/den.13530   DOI
11 He, Qi, et al. "Deep learning-based anatomical site classification for upper gastrointestinal endoscopy." International Journal of Computer Assisted Radiology and Surgery 15, pp 1085-1094 May 2020. DOI: https://doi.org/10.1007/s11548-020-02148-5   DOI
12 Takiyama, Hirotoshi, et al. "Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks." Scientific reports 8, no. 1 pp 1-8, May 2018 DOI: https://doi.org/10.1038/s41598-018-25842-6   DOI
13 Wu, Lianlian, et al. "Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy." Gut 68, no. 12 pp 2161-2169 DOI: http://dx.doi.org/10.1136/gutjnl-2018-317366   DOI
14 Tan, Mingxing, and Quoc V. Le. "Efficientnet: Rethinking model scaling for convolutional neural networks." arXiv preprint arXiv:1905.11946, 2019.
15 Igarashi, Shohei, et al. "Anatomical classification of upper gastrointestinal organs under various image capture conditions using AlexNet." Computers in Biology and Medicine 124 p.103950, Sep 2020 DOI: https://doi.org/10.1016/j.compbiomed.2020.103950   DOI
16 Januszewicz, Wladyslaw, and Michal F. Kaminski. "Quality indicators in diagnostic upper gastrointestinal endoscopy." Therapeutic Advances in Gastroenterology 13 p.1756284820916693, May 2020 DOI: https://doi.org/10.1177/1756284820916693   DOI
17 Woo, Sanghyun, et al. "Cbam: Convolutional block attention module." Proceedings of the European conference on computer vision (ECCV). pp. 3-19. 2018
18 Cubuk, Ekin D., Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le. "Autoaugment: Learning augmentation policies from data." arXiv preprint arXiv:1805.09501, 2018.
19 Sin-ae Lee, Kim, Dong-hyun, and Hyun-chong Cho. "Deep Learning based Gastric Lesion Classification System using Data Augmentation" The Transactions of The Korean Institute of Electrical Engineers 69, no. 7 pp. 1033-1039 , 2020, DOI : 10.5370/KIEE.2020.69.7.1033   DOI
20 He, Tong, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li. "Bag of tricks for image classification with convolutional neural networks." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 558-567. 2019.
21 Selvaraju, Ramprasaath R., Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. "Grad-cam: Visual explanations from deep networks via gradient-based localization." In Proceedings of the IEEE international conference on computer vision, pp. 618-626. 2017.
22 Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-excitation networks." Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132-7141, 2018.