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Optimization assisted framework for thyroid detection and classification: A new ensemble technique.

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Summary

This study introduces an advanced ultrasound-based method for diagnosing thyroid disorders. The new approach significantly improves diagnostic accuracy using Recurrent Neural Network (RNN) with Adaptive Elephant Herding Optimization (AEHO).

Keywords:
ClassificationDimensionality reductionFeature extractionOptimizationThyroid detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Biomedical Signal Processing

Background:

  • Thyroid disorder diagnosis relies heavily on imaging techniques like ultrasound (US).
  • Current diagnostic methods for thyroid disorders are still developing.
  • Accurate and efficient classification of thyroid abnormalities is crucial for patient outcomes.

Purpose of the Study:

  • To develop a novel, multi-phase approach for thyroid disorder diagnosis using ultrasound data.
  • To enhance the accuracy and efficiency of thyroid image and data analysis.
  • To compare the performance of the proposed diagnostic model against existing methods.

Main Methods:

  • Feature extraction from ultrasound images using Grey Level Co-occurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRM), Local Binary Pattern (LBP), and Local Tetra Patterns (LTrP).
  • Extraction of higher-order statistical features (skewness, kurtosis, entropy, moment) from thyroid data.
  • Dimensionality reduction using Linear Discriminant Analysis (LDA).
  • Classification using an ensemble of Support Vector Machine (SVM) and Neural Network (NN) for image features, and an Adaptive Elephant Herding Algorithm (AEHO)-optimized Recurrent Neural Network (RNN) for data features.

Main Results:

  • The proposed RNN + AEHO model demonstrated superior performance compared to various existing methods.
  • Significant improvements in diagnostic accuracy were observed, with the RNN + AEHO model outperforming others by up to 8.53% in mean value.
  • The multi-phase approach effectively addresses the curse of dimensionality and enhances classification accuracy.

Conclusions:

  • The developed ultrasound-based diagnostic approach offers a promising advancement in thyroid disorder detection.
  • The integration of advanced feature extraction, dimensionality reduction, and optimized deep learning models (RNN + AEHO) yields high diagnostic performance.
  • This method provides a robust and accurate tool for classifying thyroid disorders, potentially improving clinical decision-making.