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

doi:10.1109/CBMS.2018.00068

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 proceeding › Conference contribution

6 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 language	English
Title of host publication	Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
Editors	Bridget Kane, Jaakko Hollmen, Carolyn McGregor, Paolo Soda
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	351-356
Number of pages	6
ISBN (Electronic)	9781538660607
DOIs	https://doi.org/10.1109/CBMS.2018.00068
Publication status	Published - 2018 Jul 20
Event	31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018 - Karlstad, Sweden Duration: 2018 Jun 18 → 2018 Jun 21

Publication series

Name	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Volume	2018-June
ISSN (Print)	1063-7125

Other

Other	31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018
Country/Territory	Sweden
City	Karlstad
Period	18/6/18 → 18/6/21

Keywords

convolutional neural network
deep learning
endoscopy
gastric ulcer
ulcer detection

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Computer Science Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CBMS.2018.00068

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 (pp. 351-356). (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CBMS.2018.00068

A Novel Gastric Ulcer Differentiation System Using Convolutional Neural Networks. / Sun, Jee Young; Lee, Sang Won; Kang, Mun Cheon; Kim, Seung Wook; Kim, Seung Young; Ko, Sung Jea.

Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018. ed. / Bridget Kane; Jaakko Hollmen; Carolyn McGregor; Paolo Soda. Institute of Electrical and Electronics Engineers Inc., 2018. p. 351-356 (Proceedings - IEEE Symposium on Computer-Based Medical Systems; Vol. 2018-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sun, JY, Lee, SW, Kang, MC, Kim, SW, Kim, SY & Ko, SJ 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. Proceedings - IEEE Symposium on Computer-Based Medical Systems, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., pp. 351-356, 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, Karlstad, Sweden, 18/6/18. https://doi.org/10.1109/CBMS.2018.00068

Sun JY, Lee SW, Kang MC, Kim SW, Kim SY, Ko SJ. A Novel Gastric Ulcer Differentiation System Using Convolutional Neural Networks. In Kane B, Hollmen J, McGregor C, Soda P, editors, Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 351-356. (Proceedings - IEEE Symposium on Computer-Based Medical Systems). https://doi.org/10.1109/CBMS.2018.00068

Sun, Jee Young ; Lee, Sang Won ; Kang, Mun Cheon ; Kim, Seung Wook ; Kim, Seung Young ; Ko, Sung Jea. / A Novel Gastric Ulcer Differentiation System Using Convolutional Neural Networks. Proceedings - 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018. editor / Bridget Kane ; Jaakko Hollmen ; Carolyn McGregor ; Paolo Soda. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 351-356 (Proceedings - IEEE Symposium on Computer-Based Medical Systems).

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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.",

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N1 - Funding Information: This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (2014-0-00077, Development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis). Publisher Copyright: © 2018 IEEE.

PY - 2018/7/20

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N2 - 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.

AB - 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.

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A Novel Gastric Ulcer Differentiation System Using Convolutional Neural Networks

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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