Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images

Jisu Hong; Bo Yong Park; Hyunjin Park

doi:10.1109/EMBC.2017.8037461

Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images

Jisu Hong
, Bo Yong Park
, Hyunjin Park

Department of Electronic and Electrical Engineering

Sungkyunkwan University

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

62 Scopus citations

Abstract

Barrett's esophagus is a diseased condition with abnormal changes of the cells in the esophagus. Intestinal metaplasia (IM) and gastric metaplasia (GM) are two sub-classes of Barrett's esophagus. As IM can progress to the esophageal cancer, the neoplasia (NPL), developing methods for classifying between IM and GM are important issues in clinical practice. We adopted a deep learning (DL) algorithm to classify three conditions of IM, GM, and NPL based on endimicroscopy images. We constructed a convolutional neural network (CNN) architecture to distinguish among three classes. A total of 262 endomicroscopy imaging data of Barrett's esophagus were obtained from the international symposium on biomedical imaging (ISBI) 2016 challenge. 155 IM, 26 GM and 55 NPL cases were used to train the architecture. We implemented image distortion to augment the sample size of the training data. We tested our proposed architecture using the 26 test images that include 17 IM, 4 GM and 5 NPL cases. The classification accuracy was 80.77%. Our results suggest that CNN architecture could be used as a good classifier for distinguishing endomicroscopy imaging data of Barrett's esophagus.

Original language	English
Title of host publication	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2892-2895
Number of pages	4
ISBN (Electronic)	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8037461
State	Published - 13 Sep 2017
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: 11 Jul 2017 → 15 Jul 2017

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	11/07/17 → 15/07/17

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1109/EMBC.2017.8037461

Cite this

Hong, J., Park, B. Y., & Park, H. (2017). Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (pp. 2892-2895). Article 8037461 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8037461

Hong, Jisu ; Park, Bo Yong ; Park, Hyunjin. / Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2892-2895 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images",

abstract = "Barrett's esophagus is a diseased condition with abnormal changes of the cells in the esophagus. Intestinal metaplasia (IM) and gastric metaplasia (GM) are two sub-classes of Barrett's esophagus. As IM can progress to the esophageal cancer, the neoplasia (NPL), developing methods for classifying between IM and GM are important issues in clinical practice. We adopted a deep learning (DL) algorithm to classify three conditions of IM, GM, and NPL based on endimicroscopy images. We constructed a convolutional neural network (CNN) architecture to distinguish among three classes. A total of 262 endomicroscopy imaging data of Barrett's esophagus were obtained from the international symposium on biomedical imaging (ISBI) 2016 challenge. 155 IM, 26 GM and 55 NPL cases were used to train the architecture. We implemented image distortion to augment the sample size of the training data. We tested our proposed architecture using the 26 test images that include 17 IM, 4 GM and 5 NPL cases. The classification accuracy was 80.77\%. Our results suggest that CNN architecture could be used as a good classifier for distinguishing endomicroscopy imaging data of Barrett's esophagus.",

author = "Jisu Hong and Park, \{Bo Yong\} and Hyunjin Park",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",

year = "2017",

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Hong, J, Park, BY & Park, H 2017, Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images. in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings., 8037461, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 2892-2895, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Republic of, 11/07/17. https://doi.org/10.1109/EMBC.2017.8037461

Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images. / Hong, Jisu; Park, Bo Yong; Park, Hyunjin.
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2892-2895 8037461 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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

TY - GEN

T1 - Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images

AU - Hong, Jisu

AU - Park, Bo Yong

AU - Park, Hyunjin

PY - 2017/9/13

Y1 - 2017/9/13

N2 - Barrett's esophagus is a diseased condition with abnormal changes of the cells in the esophagus. Intestinal metaplasia (IM) and gastric metaplasia (GM) are two sub-classes of Barrett's esophagus. As IM can progress to the esophageal cancer, the neoplasia (NPL), developing methods for classifying between IM and GM are important issues in clinical practice. We adopted a deep learning (DL) algorithm to classify three conditions of IM, GM, and NPL based on endimicroscopy images. We constructed a convolutional neural network (CNN) architecture to distinguish among three classes. A total of 262 endomicroscopy imaging data of Barrett's esophagus were obtained from the international symposium on biomedical imaging (ISBI) 2016 challenge. 155 IM, 26 GM and 55 NPL cases were used to train the architecture. We implemented image distortion to augment the sample size of the training data. We tested our proposed architecture using the 26 test images that include 17 IM, 4 GM and 5 NPL cases. The classification accuracy was 80.77%. Our results suggest that CNN architecture could be used as a good classifier for distinguishing endomicroscopy imaging data of Barrett's esophagus.

AB - Barrett's esophagus is a diseased condition with abnormal changes of the cells in the esophagus. Intestinal metaplasia (IM) and gastric metaplasia (GM) are two sub-classes of Barrett's esophagus. As IM can progress to the esophageal cancer, the neoplasia (NPL), developing methods for classifying between IM and GM are important issues in clinical practice. We adopted a deep learning (DL) algorithm to classify three conditions of IM, GM, and NPL based on endimicroscopy images. We constructed a convolutional neural network (CNN) architecture to distinguish among three classes. A total of 262 endomicroscopy imaging data of Barrett's esophagus were obtained from the international symposium on biomedical imaging (ISBI) 2016 challenge. 155 IM, 26 GM and 55 NPL cases were used to train the architecture. We implemented image distortion to augment the sample size of the training data. We tested our proposed architecture using the 26 test images that include 17 IM, 4 GM and 5 NPL cases. The classification accuracy was 80.77%. Our results suggest that CNN architecture could be used as a good classifier for distinguishing endomicroscopy imaging data of Barrett's esophagus.

UR - https://www.scopus.com/pages/publications/85032210988

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DO - 10.1109/EMBC.2017.8037461

M3 - Conference contribution

C2 - 29060502

AN - SCOPUS:85032210988

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 2892

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BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017

Y2 - 11 July 2017 through 15 July 2017

ER -

Hong J, Park BY, Park H. Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2892-2895. 8037461. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2017.8037461

Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images

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