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A comparative analysis of deep learning architectures for thyroid tissue classification with hyperspectral imaging.

Matheus de Freitas Oliveira Baffa1, Denise Maria Zezell2, Luciano Bachmann1

  • 1São Paulo State University, Ribeirão Preto, Brazil.

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|August 26, 2025
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Summary

This study integrates micro-Fourier Transform Infrared (micro-FTIR) spectroscopy with deep learning for thyroid tissue analysis. One-dimensional Convolutional Neural Networks (1D-CNN) demonstrated superior accuracy in classifying goiter, cancerous, and healthy thyroid tissues.

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Area of Science:

  • Medical diagnostics
  • Biomolecular analysis
  • Spectroscopy

Background:

  • Hyperspectral imaging (HSI) offers potential in medical diagnostics by analyzing spectral information for biomolecular differentiation in tissues.
  • Analyzing high-dimensional HSI data presents significant challenges.
  • Deep learning, including recurrent neural networks (RNN) and convolutional neural networks (CNN), is crucial for complex medical data analysis.

Purpose of the Study:

  • To introduce a novel approach integrating micro-Fourier Transform Infrared (micro-FTIR) spectroscopy with deep learning for thyroid tissue classification.
  • To compare the performance of RNN, Fully Convolutional Neural Network (FCNN), and 1D-CNN in region-based classification of thyroid tissues.
  • To evaluate the precision and accuracy of these deep learning models in identifying goiter, cancerous, and healthy thyroid tissue types.

Main Methods:

  • Developed and evaluated three deep learning architectures: RNN, FCNN, and 1D-CNN.
  • Utilized micro-FTIR spectroscopy to obtain spectral data from thyroid tissue samples.
  • Employed a dataset of 60 patients and evaluated models using grouped 10-fold cross-validation for robust performance assessment.

Main Results:

  • The 1D-CNN model achieved the highest accuracy at 97.60% in classifying thyroid tissue spectral data.
  • RNN and FCNN models achieved accuracies of 96.88% and 93.66%, respectively.
  • The study demonstrated superior performance of 1D-CNN in precise region-based tissue classification.

Conclusions:

  • The integration of micro-FTIR spectroscopy and deep learning, particularly 1D-CNN, significantly enhances the precision of thyroid pathology analysis.
  • This approach offers a powerful tool for accurate differentiation of various thyroid tissue conditions.
  • The findings underscore the potential of deep learning in advancing medical diagnostics through spectral data analysis.