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Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection.

Musatafa Abbas Abbood Albadr1, Sabrina Tiun1, Masri Ayob1

  • 1CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.

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Summary
This summary is machine-generated.

This study introduces an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) for fast and accurate COVID-19 detection using chest X-rays. The OGA-ELM achieved 100% accuracy, offering an efficient alternative to CT scans.

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Chest Computed Tomography (CT) is effective for COVID-19 detection but is costly and time-consuming.
  • Chest X-rays offer a faster, cheaper, and more accessible alternative for diagnosing COVID-19.
  • Existing machine learning methods for X-ray-based COVID-19 detection, while accurate, demand significant computational resources.

Purpose of the Study:

  • To develop and evaluate an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) for COVID-19 detection using chest X-ray images.
  • To assess the efficiency and accuracy of OGA-ELM compared to traditional methods.
  • To explore the potential of OGA-ELM in medical diagnostic applications, specifically for COVID-19.

Main Methods:

  • Utilized chest X-ray images for COVID-19 detection, leveraging the accessibility and speed of this imaging modality.
  • Employed an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) model, incorporating random, K-tournament, and roulette wheel selection criteria.
  • Applied Principal Component Analysis (PCA) for dimensionality reduction of Histogram of Oriented Gradient (HOG) features.

Main Results:

  • The OGA-ELM model achieved a perfect accuracy of 100.00% in detecting COVID-19 from chest X-ray images.
  • Demonstrated significantly fast computation times, indicating high efficiency.
  • Successfully classified a benchmark dataset of 188 chest X-ray images into healthy and COVID-19 categories.

Conclusions:

  • The OGA-ELM presents a highly accurate and computationally efficient method for COVID-19 detection using chest X-rays.
  • This approach offers a viable and cost-effective alternative to CT scans for rapid COVID-19 diagnosis.
  • The study highlights the potential of OGA-ELM in medical diagnostics, paving the way for its application in similar disease detection tasks.