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Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography



Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography.


A total of 6520 gastric X-ray images obtained from 815 subjects were analyzed. We designed a deep convolutional neural network (DCNN)-based gastritis detection scheme and evaluated the effectiveness of our method. The detection performance of our method was compared with that of ABC (D) stratification.


Sensitivity, specificity, and harmonic mean of sensitivity and specificity of our method were 0.962, 0.983, and 0.972, respectively, and those of ABC (D) stratification were 0.925, 0.998, and 0.960, respectively. Although there were 18 false negative cases in ABC (D) stratification, 14 of those 18 cases were correctly classified into the positive group by our method.


Deep learning techniques may be effective for evaluation of gastritis/non-gastritis. Collaborative use of DCNN-based gastritis detection systems and ABC (D) stratification will provide more reliable gastric cancer risk information.

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Fig. 1
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Deep convolutional neural network


Harmonic mean

H. pylori :

Helicobacter pylori



ROC curve:

Receiver operating characteristic curve


Region of interest






Upper gastrointestinal endoscopy


Double-contrast upper gastrointestinal barium X-ray radiography


  1. 1.

    Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012: Globocan 2012. Int J Cancer. 2015;136:E359–86.

    Article  CAS  Google Scholar 

  2. 2.

    Jung K-W, Won Y-J, Kong H-J, et al. Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2012. Cancer Res Treat. 2015;47:127–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    The EUROGAST Study Group. An international association between Helicobacter pylori infection and gastric cancer. Lancet Elsevier. 1993;341:1359–63.

    Article  Google Scholar 

  4. 4.

    Shimoyama T, Aoki M, Sasaki Y, et al. ABC screening for gastric cancer is not applicable in a Japanese population with high prevalence of atrophic gastritis. Gastric Cancer. 2012;15:331–4.

    Article  PubMed  Google Scholar 

  5. 5.

    Zhu R, Chen K, Zheng Y-Y, et al. Meta-analysis of the efficacy of probiotics in Helicobacter pylori eradication therapy. World J Gastroenterol. Baishideng Publishing Group Inc. 2014;20:18013–21.

    Article  Google Scholar 

  6. 6.

    Kudo T, Kakizaki S, Sohara N, et al. Analysis of ABC (D) stratification for screening patients with gastric cancer. World J Gastroenterol. Baishideng Publishing Group Inc. 2011;17:4793–8.

    Article  Google Scholar 

  7. 7.

    Miura K, Okada H, Kouno Y, et al. Actual status of involvement of Helicobacter pylori infection that developed gastric cancer from group A of ABC (D) stratification—study of early gastric cancer cases that underwent endoscopic submucosal dissection. Digestion Karger Publishers. 2016;94:17–23.

    Article  CAS  Google Scholar 

  8. 8.

    Oshima A, Hirata N, Ubukata T, et al. Evaluation of a mass screening program for stomach cancer with a case–control study design. Int J Cancer. Wiley Subscription Services, Inc., A Wiley Company. 1986;38:829–33.

    CAS  Google Scholar 

  9. 9.

    Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems. Lake Tahoe, Nevada: Curran Associates Inc.; 2012. pp. 1097–105.

  10. 10.

    Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;10:257–73.

    Article  PubMed  Google Scholar 

  11. 11.

    LeCun Yann, Bengio Yoshua, Hinton Geoffrey. Deep learning. Nature. 2015;521:436–44.

    Article  CAS  Google Scholar 

  12. 12.

    Yamamichi N, Hirano C, Takahashi Y, et al. Comparative analysis of upper gastrointestinal endoscopy, double-contrast upper gastrointestinal barium X-ray radiography, and the titer of serum anti-Helicobacter pylori IgG focusing on the diagnosis of atrophic gastritis. Gastric Cancer. Springer Japan. 2016;19:670–5.

    Article  Google Scholar 

  13. 13.

    Itoh T, Saito M, Marugami N, et al. Correlation between the ABC classification and radiological findings for assessing gastric cancer risk. Jpn J Radiol. Springer Japan. 2015;33:636–44.

    Article  CAS  Google Scholar 

  14. 14.

    Dheer S, Levine MS, Redfern RO, et al. Radiographically diagnosed antral gastritis: findings in patients with and without Helicobacter pylori infection. Br J Radiol. British Institute of Radiology. 2002;75:805–11.

    Article  CAS  Google Scholar 

  15. 15.

    Kimura K, Takemoto T. An endoscopic recognition of the atrophic border and its significance in chronic gastritis. Endoscopy. © Georg Thieme Verlag, Stuttgart. 1969;1:87–97.

    Google Scholar 

  16. 16.

    Kimura K. Chronological transition of the fundic-pyloric border determined by stepwise biopsy of the lesser and greater curvatures of the stomach. Gastroenterology. 1972;63:584–92.

    CAS  Article  Google Scholar 

  17. 17.

    Cortes C. Support-vector networks. Mach Learn Springer. 1995;20:273–97.

    Google Scholar 

  18. 18.

    Otsu N. A threshold selection method from gray-level. IEEE Trans Syst Man Cybern. 1979;9:62–6.

    Article  Google Scholar 

  19. 19.

    Venegas-Barrera CS, Manjarrez J. Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV. 2004.

  20. 20.

    Jia Y, Shelhamer E, Caffe DJ. Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia. Orlando, Florida, USA: ACM; 2014. pp. 675–8.

  21. 21.

    Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.

    Article  PubMed  Google Scholar 

  22. 22.

    Sugano K. Screening of gastric cancer in Asia. Best Pract Res Clin Gastroenterol. Baillière Tindall. 2015;29:895–905.

    Article  Google Scholar 

  23. 23.

    Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. Radiological Society of North America. 2017;284:574–82.

    Google Scholar 

  24. 24.

    Kim KH, Choi SH, Park S-H. Improving arterial spin labeling by using deep learning. Radiology. Radiological Society of North America. 2018;287:658–66.

    Google Scholar 

  25. 25.

    Larson DB, Chen MC, Lungren MP, et al. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. Radiological Society of North America. 2018;287:313–22.

    Google Scholar 

  26. 26.

    Ishihara K, Ogawa T, Haseyama M. Classification of gastric cancer risk from X-ray images based on efficient image features related to serum Hp antibody level and serum PG levels. ITE Trans Media Technol Appl. 2016;4:337–48.

    Article  Google Scholar 

  27. 27.

    Ishihara K, Ogawa T, Haseyama M. Helicobacter pylori infection detection from gastric X-ray images based on feature fusion and decision fusion. Comput Biol Med. Elsevier Ltd. 2017;84:69–78.

    Article  Google Scholar 

  28. 28.

    Togo R, Ishihara K, Mabe K, et al. Preliminary study of automatic gastric cancer risk classification from photofluorography. World J Gastrointest Oncol Febr World J Gastrointest Oncol. 2018;15:62–70.

    Article  Google Scholar 

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The clinical data were acquired at The University of Tokyo Hospital in Japan. This study was partly supported by Global Station for Big Data and Cybersecurity, a project of Global Institution for Collaborative Research and Education at Hokkaido University JSPS KAKENHI Grant number JP17H01744.

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Correspondence to Katsuhiro Mabe.

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Togo, R., Yamamichi, N., Mabe, K. et al. Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography. J Gastroenterol 54, 321–329 (2019).

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  • Deep convolutional neural network
  • Artificial intelligence
  • Gastritis
  • Double-contrast upper gastrointestinal barium X-ray radiography