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Related Experiment Videos

Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable

Wulfran Fendzi Mbasso1, Ambe Harrison2, Zokir Mamadiyarov3

  • 1Technology and Applied Sciences Laboratory, U.I.T. Of Douala, University of Douala, Cameroon.

Biomedical Engineering and Computational Biology
|June 9, 2026
PubMed
Summary

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This summary is machine-generated.

A new hybrid model, the Harmonic Mean Optimizer-Convolutional Neural Network-Support Vector Machine (HMO-CNN-SVM), enhances skin lesion classification for automated skin cancer detection. This approach optimizes CNN hyperparameters and improves decision boundaries for accurate and efficient analysis.

Area of Science:

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

Background:

  • Accurate skin lesion categorization is challenging in medical image analysis, especially with limited data and computational constraints.
  • Automated skin cancer detection requires robust and efficient classification models.

Purpose of the Study:

  • To develop a scalable hybrid model for improved skin lesion categorization and automated skin cancer detection.
  • To optimize Convolutional Neural Network (CNN) hyperparameters using the Harmonic Mean Optimizer (HMO) for enhanced classification performance.
  • To leverage Support Vector Machine (SVM) on CNN feature embeddings for sharper decision boundaries.

Main Methods:

  • A hybrid model termed HMO-CNN-SVM was developed, combining CNN, HMO, and SVM.
Keywords:
computer-aided diagnosisconvolutional neural network (CNN)feature extractionharmonic mean optimizer (HMO)skin cancer diagnosissupport vector machine (SVM)

Related Experiment Videos

  • HMO was employed to optimize key CNN hyperparameters (learning rate, batch size, kernel configuration).
  • SVM was applied to CNN feature embeddings, further refined by HMO, to enhance classification accuracy.

    • Main Results:

    • The HMO-CNN-SVM model achieved a 95.02% accuracy on the ACS skin lesion dataset and ISIC 2018 benchmarks.
    • Experiments demonstrated robust performance and consistent generalization across 5-fold cross-validation.
    • The model exhibits significant parallelism potential, suitable for GPU clusters and cloud-based training.

    Conclusions:

    • The proposed HMO-CNN-SVM model offers a scalable and effective solution for skin lesion classification.
    • The hybrid approach provides both diagnostic dependability and computational tractability for automated skin cancer detection.
    • The framework is well-suited for high-performance and distributed computing environments.