Finding Small-Bowel Lesions: Challenges in Endoscopy-Image-Based Learning Systems

Jungmo Ahn, Huynh Nguyen Loc, Rajesh Krishna Balan, Youngki Lee, Jeonggil Ko

Research output: Contribution to specialist publicationArticle

3 Citations (Scopus)

Abstract

Capsule endoscopy identifies damaged areas in a patient's small intestine but often outputs poor-quality images or misses lesions, leading to either misdiagnosis or repetition of the lengthy procedure. The authors propose applying deep-learning models to automatically process the captured images and identify lesions in real time, enabling the capsule to take additional images of a specific location, adjust its focus level, or improve image quality. The authors also describe the technical challenges in realizing a viable automated capsule-endoscopy system.

Original languageEnglish
Pages68-76
Number of pages9
Volume51
No.5
Specialist publicationComputer
DOIs
Publication statusPublished - 2018 May

Bibliographical note

Funding Information:
This work was supported by the Korean Ministry of Science and ICT under the ITRC program (IITP-2018-2016-0-00309-002) and also by the DGIST Research and Development Program (CPS Global Center) for the project “Identifying Unmet

Publisher Copyright:
© 1970-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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