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Revealing the Boundaries of Selected Gastro-Intestinal (GI) Organs by Implementing CNNs in Endoscopic Capsule Images.

Sofia A AthanasiouEleftheria S SergakiAndreas A PolydorouAlexios A PolydorouGeorge S StavrakakisNikolaos M AfentakisIoannis O VardiambasisMichail E Zervakis
Published in: Diagnostics (Basel, Switzerland) (2023)
Our experimental results of independent validation demonstrate that the best of our developed models addressed this topological problem by exhibiting an overall sensitivity (96.55%) and specificity of (94.73%) in the esophagus, (81.08% sensitivity and 96.55% specificity) in the stomach, (89.65% sensitivity and 97.89% specificity) in the small intestine and (100% sensitivity and 98.94% specificity) in the colon. The average macro accuracy is 95.56%, the average macro sensitivity is 91.82%.
Keyphrases
  • machine learning
  • optical coherence tomography
  • quality improvement