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

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

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 image...

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Main Authors: AHN, Jungmo, HUYNH, Loc Nguyen, BALAN, Rajesh Krishna, LEE, Youngki, KO, JeongGil
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
Subjects:
Medical Sciences
Software Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/4053
https://ink.library.smu.edu.sg/context/sis_research/article/5056/viewcontent/08364650__1_.pdf
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.sis_research-50562018-12-19T06:40:03Z Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems AHN, Jungmo HUYNH, Loc Nguyen BALAN, Rajesh Krishna LEE, Youngki KO, JeongGil 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. 2018-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4053 info:doi/10.1109/MC.2018.2381116 https://ink.library.smu.edu.sg/context/sis_research/article/5056/viewcontent/08364650__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Medical Sciences Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Medical Sciences
Software Engineering
spellingShingle Medical Sciences
Software Engineering
AHN, Jungmo
HUYNH, Loc Nguyen
BALAN, Rajesh Krishna
LEE, Youngki
KO, JeongGil
Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems
description 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.
format text
author AHN, Jungmo
HUYNH, Loc Nguyen
BALAN, Rajesh Krishna
LEE, Youngki
KO, JeongGil
author_facet AHN, Jungmo
HUYNH, Loc Nguyen
BALAN, Rajesh Krishna
LEE, Youngki
KO, JeongGil
author_sort AHN, Jungmo
title Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems
title_short Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems
title_full Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems
title_fullStr Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems
title_full_unstemmed Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems
title_sort finding small-bowel lesions: challenges in endoscopy-image-based learning systems
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4053
https://ink.library.smu.edu.sg/context/sis_research/article/5056/viewcontent/08364650__1_.pdf
_version_ 1770574204881600512

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