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XAI-MedNet: A Next-Generation Explainable AI Framework for Contrast-Enhanced Skin Lesion Classification via

Abdulrahman Alabduljabbar1, Tallha Akram1, Youssef N Altherwy1

  • 1Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.

Bioengineering (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an explainable AI (XAI) framework for melanoma detection, enhancing image quality and refining features for improved accuracy and interpretability in early skin cancer diagnosis.

Area of Science:

  • Medical Image Analysis
  • Artificial Intelligence in Dermatology
  • Computational Pathology

Background:

  • Melanoma detection is crucial for survival, yet current automated systems face challenges with accuracy and interpretability.
  • Explainable Artificial Intelligence (XAI) is vital for clinical trust in AI-driven medical image analysis.
  • Early-stage diagnosis of skin cancer significantly improves patient outcomes.

Purpose of the Study:

  • To develop an enhanced XAI framework for melanoma segmentation and classification.
  • To improve the accuracy and interpretability of AI models in skin lesion analysis.
  • To address limitations in current automated diagnostic systems, including high error margins and lack of transparency.

Main Methods:

  • A novel metaheuristic contrast-stretching method was used to enhance image quality and lesion boundary clarity.
Keywords:
BAT optimizationCNN modelsExplainable AIartificial bee colony optimizationevolutionary techniquesfeature selectioninterpretable machine learningskin lesion classificationwhale optimization

Related Experiment Videos

  • Features were extracted from pre-trained deep models (DenseNet-201, Inception-ResNet v2, NASNet-Mobile) and fused.
  • An entropy-controlled whale optimization algorithm was employed for discriminative feature selection, followed by multi-classifier classification.

    • Main Results:

    • The proposed framework demonstrated superior performance over existing methods in accuracy, sensitivity, specificity, and F1-score.
    • The approach enhanced lesion boundary distinguishability and segmentation accuracy.
    • Feature refinement using entropy-controlled whale optimization resulted in a compact and informative feature set.

    Conclusions:

    • The developed XAI framework offers a more explainable, transparent, and accurate diagnostic pipeline for melanoma detection.
    • The study successfully addressed key challenges in AI-based skin lesion analysis, improving clinical decision support.
    • The integration of image enhancement, feature fusion, and optimized feature selection provides a robust approach for early skin cancer diagnosis.