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Related Experiment Videos

An End-to-End Deep Learning System for Gastrointestinal Bleeding Detection and Quantification in Wireless Capsule

Mujeeb Rahman Kanhira Kadavath1, Aman Kitaz1, Nour El Houda Benyahia1

  • 1Department of Biomedical Engineering, College of Engineering & Information Technology (CEIT), Ajman University, Ajman P.O. Box 346, United Arab Emirates.

Diagnostics (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

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A new deep learning framework accurately detects and quantifies gastrointestinal bleeding in wireless capsule endoscopy (WCE) images. This automated system improves diagnostic consistency and reduces manual review time for clinicians.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Gastrointestinal bleeding detection via wireless capsule endoscopy (WCE) is crucial but labor-intensive.
  • Manual review of WCE images is time-consuming and prone to missed diagnoses.

Purpose of the Study:

  • To develop and evaluate a deep learning framework for automated bleeding detection, localization, and quantification in WCE.
  • To reduce the diagnostic burden and improve accuracy in WCE analysis.

Main Methods:

  • Integrated three deep learning models: 2D-CNN for frame classification, 3D-CNN for temporal analysis, and U-Net for pixel-level segmentation.
  • Trained and validated models using expert-annotated WCE datasets with pixel-level ground truth.

Main Results:

Keywords:
2D-CNN3D-CNNU-Net modelconvolutional neural networkgastro intestinal bleedingimage segmentationmachine learningquantification of intestinal bleedingwireless capsule endoscopy

Related Experiment Videos

  • 2D-CNN and 3D-CNN achieved high classification performance (AUCs 0.9986 and 0.9971).
  • U-Net demonstrated strong segmentation accuracy (Dice 0.93, IoU 0.8677, overall 97.25%).
  • The integrated framework surpassed previous methods in bleeding detection, localization, and quantification.

Conclusions:

  • The end-to-end deep learning framework provides accurate automated bleeding detection and severity assessment in WCE.
  • Potential to enhance clinical decision-making and streamline gastrointestinal diagnostic workflows.