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Role of Optimal Features Selection with Machine Learning Algorithms for Chest X-ray Image Analysis.

Mohini Manav1,2, Monika Goyal1, Anuj Kumar2

  • 1Department of Physics, GLA University, Mathura, Uttar Pradesh, India.

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|August 14, 2023
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Summary

This study optimized feature selection and classification for COVID-19 detection from chest X-rays. The combination of Particle Swarm Optimization and Support Vector Machine achieved 100% accuracy, aiding early diagnosis.

Keywords:
Artificial intelligencechest X-rayfeature selectionimage classificationmachine learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Accurate and early detection of COVID-19 is crucial for patient management.
  • Chest X-ray (CXR) imaging is a widely used diagnostic tool.
  • Automated analysis of CXR images can assist in rapid COVID-19 diagnosis.

Purpose of the Study:

  • To classify chest X-ray (CXR) images into COVID-positive and normal categories.
  • To determine the optimal features and classification algorithms for accurate COVID-19 detection.
  • To develop a computer-aided diagnosis (CAD) system for COVID-19.

Main Methods:

  • Utilized contrast limited adaptive histogram equalization to enhance CXR image details.
  • Extracted features using pyFeats, including statistical, texture, and transform-based methods.
  • Employed nature-inspired algorithms (Grey Wolf Optimization, Particle Swarm Optimization, Genetic Algorithm) for feature selection and various classifiers (Random Forest, KNN, SVM, LightGBM) for classification.

Main Results:

  • The Support Vector Machine (SVM) classifier demonstrated superior performance across all feature selection methods.
  • Particle Swarm Optimization (PSO) yielded the best results for feature selection.
  • The combination of SVM with PSO achieved 100% accuracy, precision, recall, and F1-score.

Conclusions:

  • Optimal feature selection and classifier choice are key for accurate computer-aided diagnosis of CXR images.
  • The proposed method, using optimal features, can serve as a valuable complementary tool for radiologists.
  • This approach supports earlier disease diagnosis and more informed clinical decision-making.