Finding Small-Bowel Lesions: Challenges in Endoscopy-Image-Based Learning Systems

Jungmo Ahn, Huynh Nguyen Loc, Rajesh Krishna Balan, Youngki Lee, Jeonggil Ko

Research output: Contribution to specialist publicationArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages68-76
Number of pages9
Volume51
No.5
Specialist publicationComputer
DOIs
Publication statusPublished - 2018 May

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Endoscopy
Image quality
Learning systems
Deep learning

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Ahn, Jungmo ; Nguyen Loc, Huynh ; Krishna Balan, Rajesh ; Lee, Youngki ; Ko, Jeonggil. / Finding Small-Bowel Lesions : Challenges in Endoscopy-Image-Based Learning Systems. In: Computer. 2018 ; Vol. 51, No. 5. pp. 68-76.
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Finding Small-Bowel Lesions : Challenges in Endoscopy-Image-Based Learning Systems. / Ahn, Jungmo; Nguyen Loc, Huynh; Krishna Balan, Rajesh; Lee, Youngki; Ko, Jeonggil.

In: Computer, Vol. 51, No. 5, 05.2018, p. 68-76.

Research output: Contribution to specialist publicationArticle

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