The Community for Technology Leaders
Green Image
Issue No. 05 - May (2018 vol. 51)
ISSN: 0018-9162
pp: 68-76
Jungmo Ahn , Ajou University
Huynh Nguyen Loc , Singapore Management University
Rajesh Krishna Balan , Singapore Management University
Youngki Lee , Singapore Management University
JeongGil Ko , Ajou University
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.
biological organs, biomedical optical imaging, endoscopes, image capture, learning (artificial intelligence), medical image processing

J. Ahn, H. Nguyen Loc, R. Krishna Balan, Y. Lee and J. Ko, "Finding Small-Bowel Lesions: Challenges in Endoscopy-Image-Based Learning Systems," in Computer, vol. 51, no. 5, pp. 68-76, 2018.
494 ms
(Ver 3.3 (11022016))