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Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning

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Summary

Optimizing parameters for multiscale fuzzy and dispersion entropy significantly boosts image classification accuracy, achieving over 95%. Multiscale fuzzy entropy generally outperforms dispersion entropy, regardless of the classifier used.

Keywords:
biomedical dataclassifiercomplexitydispersion entropyentropyfuzzy entropyimage analysisirregularitymultiscale approach

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Area of Science:

  • Image analysis and machine learning
  • Entropy-based feature extraction

Background:

  • Two-dimensional fuzzy entropy (FuzzyEn2D) and dispersion entropy (DispEn2D), along with their multiscale extensions (MFuzzyEn2D and MDispEn2D), show potential for image classification.
  • The performance of these entropy measures is highly dependent on parameter selection, which has not been extensively studied.

Purpose of the Study:

  • To investigate the impact of parameter selection on MFuzzyEn2D and MDispEn2D for image classification.
  • To determine optimal parameter settings for these entropy-based algorithms across diverse image datasets.

Main Methods:

  • Applied MFuzzyEn2D and MDispEn2D algorithms with various parameter combinations to multiple image datasets.
  • Utilized the extracted entropy values as features for a range of machine learning classifiers.
  • Analyzed algorithm parameter performance based on classification accuracy.

Main Results:

  • Optimized parameter settings enable MFuzzyEn2D and MDispEn2D to achieve state-of-the-art image classification performance, with an average maximum accuracy exceeding 95% across all tested datasets.
  • MFuzzyEn2D generally demonstrated superior classification performance compared to MDispEn2D.
  • The choice of machine learning classifier had a negligible impact on the classification results derived from both entropy algorithms.

Conclusions:

  • Specific parameter tuning is crucial for maximizing the effectiveness of MFuzzyEn2D and MDispEn2D in image classification.
  • These entropy-based methods, particularly MFuzzyEn2D, offer a powerful approach for textural analysis and image classification.
  • The findings suggest broad applicability and robustness of these entropy measures, independent of the specific classifier.