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Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework.

Joel C M Than1, Luca Saba2, Norliza M Noor3

  • 1UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Malaysia.

Computers in Biology and Medicine
|August 22, 2017
PubMed
Summary

This study introduces a two-stage CADx system for lung disease risk stratification, improving accuracy by combining lung delineation and morphology-based characterization. The novel approach achieves 99.53% accuracy, outperforming existing methods.

Keywords:
AmalgamationCADx cascaded systemComputer tomographyGabor transformsLung cancerPerformanceRiesz transformsRisk stratification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Pulmonary Medicine

Background:

  • Lung disease risk stratification is crucial for diagnosis and treatment planning.
  • Manual methods face challenges due to large data, observer variability, and limited feature integration.
  • Existing machine learning models often lack comprehensive feature amalgamation.

Purpose of the Study:

  • To develop and evaluate a two-stage Computer-Aided Diagnosis (CADx) system for lung disease risk stratification.
  • To address limitations of manual stratification and conventional machine learning approaches.
  • To improve accuracy and robustness in identifying lung disease risks.

Main Methods:

  • A semi-automated lung delineation subsystem (LDS) using entropy-based region extraction for CT slices.
  • Morphology-based lung tissue characterization using amalgamated directional (Riesz, Gabor) and texture features.
  • K-fold cross-validation (K=2, 3, 5, 10) on a database of 96 patients (15 normal, 81 diseased) across five HRCT levels.

Main Results:

  • The CADx system achieved a lung disease risk stratification accuracy of 99.53%.
  • This represents a 2% improvement over conventional non-amalgamation machine learning systems.
  • The system demonstrated high robustness with a reliability index of 0.99 and risk stratification accuracy deviation <5%.

Conclusions:

  • The proposed two-stage CADx system effectively enhances lung disease risk stratification accuracy.
  • Amalgamation of diverse features significantly improves machine learning performance in lung imaging.
  • This approach offers a robust and accurate alternative to manual methods, outperforming existing prominent studies.