TY - GEN
T1 - Finding Small-Bowel Lesions
T2 - Challenges in Endoscopy-Image-Based Learning Systems
AU - Ahn, Jungmo
AU - Nguyen Loc, Huynh
AU - Krishna Balan, Rajesh
AU - Lee, Youngki
AU - Ko, Jeonggil
N1 - Publisher Copyright:
© 1970-2012 IEEE.
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85047769070&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047769070&partnerID=8YFLogxK
U2 - 10.1109/MC.2018.2381116
DO - 10.1109/MC.2018.2381116
M3 - Article
AN - SCOPUS:85047769070
SN - 0018-9162
VL - 51
SP - 68
EP - 76
JO - Computer
JF - Computer
ER -