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.
Bibliographical noteFunding Information:
This work was supported by the Korean Ministry of Science and ICT under the ITRC program (IITP-2018-2016-0-00309-002) and also by the DGIST Research and Development Program (CPS Global Center) for the project “Identifying Unmet
All Science Journal Classification (ASJC) codes
- Computer Science(all)