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