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Optimizing the classification of biological tissues using machine learning models based on polarized data.

Carla Rodríguez1, Irene Estévez1,2, Emilio González-Arnay3,4

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This summary is machine-generated.

This study compared polarimetric datasets for tissue classification. Raw Mueller matrix elements proved superior to derived observables for building accurate recognition models for organic tissues.

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Area of Science:

  • Biomedical optics
  • Tissue characterization
  • Medical imaging

Background:

  • Polarimetric data, derived from Mueller matrices, is crucial for organic tissue characterization and disease detection.
  • Current methods transform Mueller matrices into specific observables, potentially losing vital sample information.
  • A comprehensive comparison of different polarimetric datasets is needed to identify the optimal framework for tissue classification models.

Purpose of the Study:

  • To conduct a thorough comparative analysis of 12 classification models using diverse polarimetric datasets.
  • To determine the most effective polarimetric framework for constructing robust tissue classification models.
  • To evaluate the impact of data representation (derived observables vs. raw matrix elements) on classification performance.

Main Methods:

  • Experimental Mueller matrix images were acquired from ex-vivo chicken thigh tissues (muscle, tendon, myotendinous junction, bone).
  • Three distinct polarimetric datasets were analyzed: representative literature observables, raw Mueller matrix elements, and a combined set.
  • 12 different classification models were trained and evaluated on these datasets.

Main Results:

  • Classification models built using raw Mueller matrix elements (Dataset B) demonstrated superior performance compared to those using derived observables (Dataset A).
  • The combined dataset (Dataset C) did not significantly outperform the raw Mueller matrix elements alone.
  • The study underscores the critical importance of utilizing raw Mueller matrix data for designing effective tissue classification models.

Conclusions:

  • Raw Mueller matrix elements provide a more comprehensive and informative dataset for tissue classification than derived polarimetric observables.
  • The findings suggest a shift in methodology towards using raw Mueller matrix data for improved accuracy in biomedical recognition models.
  • This research offers a valuable framework for optimizing polarimetric data selection in the development of diagnostic tools for tissue characterization.