Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems

Author

Jungmo AHN, Ajou University
Loc Nguyen HUYNH, Singapore Management UniversityFollow
Rajesh Krishna BALAN, Singapore Management UniversityFollow
Youngki LEE, Singapore Management UniversityFollow
JeongGil KO, Ajou University

Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2018

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.

Discipline

Medical Sciences | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Computer

Volume

51

Issue

5

First Page

68

Last Page

76

ISSN

0018-9162

Identifier

10.1109/MC.2018.2381116

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Citation

AHN, Jungmo; HUYNH, Loc Nguyen; BALAN, Rajesh Krishna; LEE, Youngki; and KO, JeongGil. Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems. (2018). Computer. 51, (5), 68-76.
Available at: https://ink.library.smu.edu.sg/sis_research/4053

Copyright Owner and License

Publisher

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://doi.org/10.1109/MC.2018.2381116