전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제67권7호
  • /
  • Pages.928-933
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  • 2018
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  • 1975-8359(pISSN)
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  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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위 내시경 영상을 이용한 병변 진단을 위한 딥러닝 기반 컴퓨터 보조 진단 시스템

Deep Learning based Computer-aided Diagnosis System for Gastric Lesion using Endoscope

  • Kim, Dong-hyun (Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University) ;
  • Cho, Hyun-chong (Dept. of Electronic Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence, Kangwon National University)
  • 투고 : 2018.06.04
  • 심사 : 2018.06.25
  • 발행 : 2018.07.01
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초록

Nowadays, gastropathy is a common disease. As endoscopic equipment are developed and used widely, it is possible to provide a large number of endoscopy images. Computer-aided Diagnosis (CADx) systems aim at helping physicians to identify possibly malignant abnormalities more accurately. In this paper, we present a CADx system to detect and classify the abnormalities of gastric lesions which include bleeding, ulcer, neuroendocrine tumor and cancer. We used an Inception module based deep learning model. And we used data augmentation for learning. Our preliminary results demonstrated promising potential for automatically labeled region of interest for endoscopy doctors to focus on abnormal lesions for subsequent targeted biopsy, with Az values of Receiver Operating Characteristic(ROC) curve was 0.83. The proposed CADx system showed reliable performance.

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참고문헌

  1. J. Ferlay, I. Soerjomataram, M. Ervik, R. Dikshit, S. Eser, C. Mathers, et al., "GLOBOCAN 2012 v1. 1", Cancer Incidence and mortality worldwide: IARC CancerBase, 2014.
  2. Y. L. Kim, W. H. Kim, T. J. Kim, and K. W. Choi, "Histotopographic Characterization of Chronic Gastritis Associated with Early Gastric Carcinomas", The Korean Journal of Gastroenterology, vol. 24, pp. 216-223, 1992.
  3. H. S. Kim, "Clinical Significance of Peptic Ulcer Staging and Biopsies", The Korean Journal of Gastrointestinal Endoscopy, vol. 42, pp. 24S-26S, 2011.
  4. T. C. Lee, Y. H. Lin, N. Uedo, H. P. Wang, H. T. Chang, and C. W. Hung, "Computer-aided diagnosis in endoscopy: A novel application toward automatic detection of abnormal lesions on magnifying narrowband imaging endoscopy in the stomach", in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 4430-4433, 2013.
  5. X. Liu, C. Wang, J. Bai, G. Liao, and Y. Zhao, "Huetexture-embedded region-based model for magnifying endoscopy with narrow-band imaging image segmentation based on visual features", Computer methods and programs in biomedicine, vol. 145, pp. 53-66, 2017. https://doi.org/10.1016/j.cmpb.2017.04.010
  6. Y. Cong, S. Wang, J. Liu, J. Cao, Y. Yang, and J. Luo, "Deep sparse feature selection for computer aided endoscopy diagnosis", Pattern Recognition, vol. 48, pp. 907-917, 2015. https://doi.org/10.1016/j.patcog.2014.09.010
  7. P. Guobing, X. Fang, and C. Jiaoliao, "A novel algorithm for color similarity measurement and the application for bleeding detection in WCE", International Journal of Image, Graphics and Signal Processing, vol. 3, p. 1, 2011.
  8. B. Li, M. Q. H. Meng, "Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images", IEEE Transactions on biomedical engineering, vol. 56, pp. 1032-1039, 2009. https://doi.org/10.1109/TBME.2008.2010526
  9. A. Karargyris and N. Bourbakis, "Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos", IEEE Transactions on biomedical engineering, vol. 58, pp. 2777-2786, 2011 https://doi.org/10.1109/TBME.2011.2155064
  10. M. J. Kim and H. C. Cho, "A Study of CBIR(Contentbased Image Retrieval) Computer-aided Diagnosis System of Breast Ultrasound Images using Similarity Measures of Distance", The Transactions of the Korean Institute of Electrical Engineers , vol. 66, pp. 1272-1277, 2017.
  11. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception architecture for computer vision", in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016.
  12. J. G. C. Association, "Japanese classification of gastric carcinoma: 3rd English edition," Gastric cancer, vol. 14, pp. 101-112, 2011. https://doi.org/10.1007/s10120-011-0041-5
  13. W. S. Sang, "Using the Receiver Operating Characteristic (ROC) Curve to Measure Sensitivity and Specifi city", Korean Journal of Family Medicine, vol. 30, pp. 841-842, 2009. https://doi.org/10.4082/kjfm.2009.30.11.841