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Related Experiment Video

Updated: Feb 27, 2026

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Ensemble model assisted classification of gastrointestinal bleeding using wireless capsule endoscopy.

Jolly Parikh1, Manjesh Singh1, Nupur Chugh2

  • 1Department of Computer Science and Engineering, Bharati Vidyapeeth's College of Engineering (BVCOE), New Delhi, Delhi, India.

Physical and Engineering Sciences in Medicine
|February 26, 2026
PubMed
Summary

This study introduces an automated method using an ensemble of deep learning models to analyze wireless capsule endoscopy (WCE) images for detecting gastrointestinal bleeding. The approach significantly improves diagnostic accuracy and reduces analysis time for medical professionals.

Keywords:
Bleeding detectionDeep learningDenseNet201EfficientNetB7Gastrointestinal regionImage classificationMobileNetV2VGG16Wireless capsule endoscopy

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Wireless Capsule Endoscopy (WCE) generates large datasets requiring extensive analysis for diagnosing gastrointestinal diseases.
  • Manual WCE image review is time-consuming and requires specialized expertise, highlighting the need for automated analysis tools.
  • Accurate and efficient detection of anomalies, such as bleeding, is crucial for timely patient diagnosis and treatment.

Purpose of the Study:

  • To develop and evaluate an automated approach for analyzing WCE images to detect gastrointestinal bleeding.
  • To enhance diagnostic accuracy and reduce the time required for WCE data interpretation.
  • To provide a reliable tool for medical professionals in diagnosing gastrointestinal tract diseases.

Main Methods:

  • An ensemble deep learning model was developed by combining DenseNet201, MobileNetV2, and EfficientNetB7 Convolutional Neural Networks (CNNs).
  • The ensemble model was trained and validated on a dataset comprising 1309 bleeding and 1309 non-bleeding WCE images.
  • Performance was evaluated using metrics including accuracy, precision, recall, and F1 score, and compared against individual models and a custom CNN.

Main Results:

  • The proposed CNN-based average ensemble model achieved a high accuracy of 98.74%.
  • The model demonstrated strong performance with precision, recall, and F1 scores of 98.06%, 98.83%, and 98.44%, respectively.
  • The ensemble approach outperformed individual component models and a custom-built CNN, indicating its effectiveness.

Conclusions:

  • The developed ensemble model provides an effective and automated solution for analyzing WCE images to detect gastrointestinal bleeding.
  • This approach offers a significant improvement over manual review, potentially reducing diagnostic time and increasing accuracy.
  • The findings suggest that this automated WCE image analysis method is a beneficial tool for healthcare professionals in gastroenterology.