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EndoNet: A Multiscale Deep Learning Framework for Multiple Gastrointestinal Disease Classification via Endoscopic

Omneya Attallah1,2, Muhammet Fatih Aslan3, Kadir Sabanci3

  • 1Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 21937, Egypt.

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Summary
This summary is machine-generated.

EndoNet, a novel deep learning framework, accurately classifies gastrointestinal diseases from wireless capsule endoscopy images. This AI tool enhances diagnostic accuracy for improved patient outcomes.

Keywords:
convolutional neural networksfeature fusiongastrointestinal disease classificationminimum redundancy maximum relevancenon-negative matrix factorization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Gastrointestinal (GI) disorders pose significant healthcare challenges, necessitating advanced diagnostic tools.
  • Wireless capsule endoscopy (WCE) aids GI abnormality detection, but differentiating similar lesions is difficult.
  • Existing computer-aided diagnostic (CAD) systems often lack the complexity to analyze diverse GI disease characteristics.

Purpose of the Study:

  • To develop and evaluate EndoNet, a multi-stage hybrid deep learning (DL) framework for classifying eight types of GI diseases using WCE images.
  • To improve the accuracy and interpretability of automated GI disease diagnosis.
  • To provide a generalizable AI solution for clinical decision support.

Main Methods:

  • Proposed EndoNet, a hybrid DL framework integrating features from multiple pre-trained convolutional neural networks (CNNs) (Inception, Xception, ResNet101).
  • Employed inter-layer and inter-model feature fusion, Non-Negative Matrix Factorization (NNMF) for dimensionality reduction, and Minimum Redundancy Maximum Relevance (mRMR) for feature selection.
  • Evaluated performance on the Kvasir v2 and HyperKvasir datasets using seven machine learning algorithms.

Main Results:

  • EndoNet achieved high classification accuracy, reaching up to 97.8% on the Kvasir v2 dataset and 98.4% on the HyperKvasir dataset.
  • The framework demonstrated effective feature extraction, fusion, and selection for complex GI image analysis.
  • The combination of DL and feature engineering proved superior to traditional CAD approaches.

Conclusions:

  • EndoNet integrates transfer learning, feature engineering, dimensionality reduction, and feature selection for accurate GI disease classification.
  • The proposed framework offers high accuracy, flexibility, and interpretability, making it suitable for clinical decision support.
  • EndoNet represents a powerful and generalizable AI solution for advancing WCE-based gastrointestinal diagnostics.