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Empirical Mode Decomposition-Based Deep Learning Model Development for Medical Imaging: Feasibility Study for

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Summary
This summary is machine-generated.

This study introduces a novel deep learning framework using two-dimensional Empirical Mode Decomposition (2D EMD) to improve medical image classification. Integrating 2D EMD enhances AI model performance for disease detection in gastrointestinal endoscopy images.

Keywords:
deep learningempirical mode decomposition (EMD)feature enhancementgastrointestinal (GI) endoscopyimage classificationimage processing

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging Analysis
  • Signal Processing

Background:

  • Accurate medical image classification is crucial for early disease detection.
  • Deep learning models show promise but can be further optimized for complex medical datasets.
  • Gastrointestinal endoscopic imaging presents unique challenges for automated analysis.

Purpose of the Study:

  • To develop and evaluate a novel deep learning framework incorporating two-dimensional Empirical Mode Decomposition (2D EMD) for enhanced multi-class image classification.
  • To assess the impact of 2D EMD on the performance of various deep learning architectures in classifying gastrointestinal endoscopic images.
  • To explore the potential of this approach for early disease detection in healthcare.

Main Methods:

  • A deep learning framework was designed utilizing 2D EMD to decompose medical images into intrinsic mode functions (IMFs).
  • The Kvasir dataset, comprising 8000 gastrointestinal endoscopic images across eight classes, was used for validation.
  • The efficacy of integrating 2D EMD was evaluated by comparing four established deep learning models (ResNet152, VGG19bn, MobileNetV3L, SwinTransformerV2S) with and without EMD preprocessing.

Main Results:

  • Subtracting IMFs derived from 2D EMD consistently improved accuracy, F1-score, and AUC across all evaluated deep learning models.
  • Significant performance enhancements were observed, including approximately 9% accuracy increase for ResNet152, 18% for VGG19L, 3% for MobileNetV3L, and 8% for SwinTransformerV2.
  • Explainable AI techniques (Grad-CAM) confirmed that the models focused on relevant gastrointestinal regions for accurate predictions.

Conclusions:

  • The integration of 2D EMD significantly enhances the performance of deep learning models for gastrointestinal image classification.
  • This 2D EMD-based approach offers a promising strategy for improving AI-driven medical image analysis and disease detection.
  • The framework demonstrates potential for broader applications in medical imaging and other image classification domains.