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An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological

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Machine learning, particularly convolutional neural networks, shows promise in diagnosing COVID-19 from chest X-rays. These AI methods can detect subtle lung changes, aiding in early disease detection and patient management.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • COVID-19 primarily affects the lungs, causing inflammation and potentially leading to respiratory failure and multi-organ damage.
  • Radiological pulmonary evaluation is critical for managing critically ill COVID-19 patients.
  • Interpreting radiological images requires specialized expertise from radiologists.

Purpose of the Study:

  • To investigate the efficacy of machine learning techniques for diagnosing COVID-19 using chest X-ray images.
  • To identify subtle morphological differences in the lungs of COVID-19 patients that may not be apparent to the human eye.
  • To compare the performance of various machine learning algorithms in COVID-19 detection from radiological data.

Main Methods:

  • Utilized chest X-ray images from publicly available datasets containing COVID-19 positive cases.
  • Extracted image features using the gray level co-occurrence matrix (GLCM) method.
  • Applied several machine learning classifiers including K-nearest neighbor, support vector machine, linear discrimination analysis, naïve Bayes, and convolutional neural network (CNN).

Main Results:

  • Machine learning methods demonstrated promising results in diagnosing COVID-19 from chest X-ray images.
  • Convolutional neural networks (CNNs) exhibited superior performance compared to traditional machine learning approaches.
  • AI-driven analysis of radiological images showed potential for reduced human involvement and enhanced diagnostic accuracy.

Conclusions:

  • Deep learning techniques, specifically CNNs, are effective tools for automated COVID-19 detection via chest X-rays.
  • AI can identify subtle radiological patterns indicative of COVID-19, complementing human expert interpretation.
  • The findings suggest a significant role for artificial intelligence in improving the diagnostic workflow for respiratory infections like COVID-19.