A Novel Gastric Ulcer Differentiation System Using Convolutional Neural Networks

Jee Young Sun, Sang Won Lee, Mun Cheon Kang, Seung Wook Kim, Seung Young Kim, Sung-Jea Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Gastric cancer can present itself as a gastric ulcer, which can mimic a benign gastric ulcer. In this paper, we introduce an objective and precise gastric ulcer differentiation system based on deep convolutional neural network (CNN) which can support the specialists by improving the diagnostic accuracy of the endoscopic examination of gastric ulcers. We first generated a new dataset consisting of endoscopic images of gastric ulcers and their corresponding type labels obtained by biopsy. We then design various ulcer differentiation models using classification or detection networks, and evaluate the performance of the models on the new dataset. Experimental results confirm that the classification network-based method shows performance comparable to doctors' diagnosis, and the detection network-based one, which first detects ulcer regions and then determines the type of ulcer based on the detection results, exhibits the best performance. The proposed method provides an unbiased diagnosis and it outperforms endoscopic diagnoses performed by the specialists in terms of total accuracy.

Original languageEnglish
Title of host publicationProceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
EditorsBridget Kane, Jaakko Hollmen, Carolyn McGregor, Paolo Soda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages351-356
Number of pages6
Volume2018-June
ISBN (Electronic)9781538660607
DOIs
Publication statusPublished - 2018 Jul 20
Event31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018 - Karlstad, Sweden
Duration: 2018 Jun 182018 Jun 21

Other

Other31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
CountrySweden
CityKarlstad
Period18/6/1818/6/21

Keywords

  • convolutional neural network
  • deep learning
  • endoscopy
  • gastric ulcer
  • ulcer detection

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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  • Cite this

    Sun, J. Y., Lee, S. W., Kang, M. C., Kim, S. W., Kim, S. Y., & Ko, S-J. (2018). A Novel Gastric Ulcer Differentiation System Using Convolutional Neural Networks. In B. Kane, J. Hollmen, C. McGregor, & P. Soda (Eds.), Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018 (Vol. 2018-June, pp. 351-356). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS.2018.00068