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Classification of Neural Stem Cell Activation State In Vitro using Autofluorescence
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Accurate and interpretable classification of microspectroscopy pixels using artificial neural networks.

Petru Manescu1, Young Jong Lee1, Charles Camp1

  • 1National Institute of Standards and Technology, Gaithersburg, MD 20877, USA.

Medical Image Analysis
|January 29, 2017
PubMed
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This study introduces a novel tandem Artificial Neural Network (ANN) model for pixel-level material classification in microspectroscopy. The method achieves high accuracy and interpretability by combining spectral feature identification with rule extraction.

Area of Science:

  • Spectroscopy
  • Machine Learning
  • Materials Science

Background:

  • Pixel-level material classification from microspectroscopy presents challenges in identifying discriminatory spectral features.
  • Developing accurate and interpretable models linking spectra to class labels is crucial.

Purpose of the Study:

  • To design a supervised classifier using a tandem of Artificial Neural Network (ANN) models for accurate and interpretable pixel-level material classification.
  • To integrate classification rule extraction methods to reduce model complexity and enhance interpretability.

Main Methods:

  • A supervised classifier was designed using a tandem of ANN models tailored to microspectroscopy data.
  • Each ANN model was designed based on the hypothesis that discriminatory features are linear combinations of spectra.
Keywords:
Artificial neural networksBCARSHyperspectral imagingMicrospectroscopyRule-based model

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  • Classification rule extraction methods were meshed with ANN models for interpretability.

    • Main Results:

    • The proposed method achieved an average accuracy of 85% using the DICE pixel label similarity metric on broadband coherent anti-Stokes Raman scattering (BCARS) microscopy cell images.
    • The generated classification rules showed an average similarity of 96% to expert-created rules, measured by the vector cosine metric.
    • The tandem ANN and decision rule configuration resulted in accurate and interpretable classification.

    Conclusions:

    • The developed tandem ANN and decision rule model effectively classifies materials at the pixel level in microspectroscopy data.
    • The approach successfully balances high classification accuracy with model interpretability.
    • This method offers a promising tool for analyzing complex spectral data in scientific research.