Deep convolutional neural network-based automated lesion detection in wireless capsule endoscopy

Yejin Jeon, Eunbyul Cho, Sehwa Moon, Seung Hoon Chae, Hae Young Jo, Tae Oh Kim, Chang Mo Moon, Jang Hwan Choi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


Because most of the capsule-endoscopic images contain normal mucous membranes, physicians spend most of their reading time observing normal areas. Thus, a significant reduction in their reading time would be possible if only the portion of the image frame for which a particular lesion is suspected can be read intensively. This study aims to develop a deep convolutional neural-network-based model capable of automatically detecting lesions in the capsule-endoscopic images of a small bowel. The proposed model consists of two deep neural networks in parallel, each of which takes in images in RGB and CIELab color spaces, respectively. The neural-networks model is based on transfer-learned GoogLeNet architecture. Our proposed algorithm showed promising results in classifying endoscopic images where lesions exist (98.56% accuracy). If the proposed algorithm is used to screen abnormal images, it is expected to reduce a physician's reading time and to improve his/her reading accuracy.

Original languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2019
EditorsFeng Lin, Hiroshi Fujita, Jong Hyo Kim
ISBN (Electronic)9781510627758
StatePublished - 2019
EventInternational Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
Duration: 7 Jan 20199 Jan 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceInternational Forum on Medical Imaging in Asia 2019

Bibliographical note

Publisher Copyright:
© 2019 SPIE.


  • Convolutional neural Networks
  • Deep neural networks
  • Lesion detection
  • Small bowel tumor
  • Small-bowel Wireless Capsule Endoscopy
  • Wireless capsule endoscopy


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