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Chest X-ray analysis empowered with deep learning: A systematic review.

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This summary is machine-generated.

This systematic review analyzes deep learning for chest X-ray analysis in pneumonia and COVID-19 detection. It highlights current trends, datasets, and future research directions for medical imaging AI.

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COVID-19Chest radiographyComputer-aided diagnosticsConvolutional Neural networksMedical image processingPneumoniaRadiographyRespiratory diseases

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Chest radiographs are crucial for diagnosing conditions like pneumonia and COVID-19.
  • Deep learning shows significant promise in medical image classification.
  • Recent growth in chest X-ray datasets and publications necessitates updated reviews.

Purpose of the Study:

  • To systematically review and analyze studies using deep learning for chest X-ray image analysis.
  • To provide a comprehensive overview of state-of-the-art deep learning solutions for pneumonia and COVID-19 detection.
  • To identify trends, datasets, challenges, and future research directions in the field.

Main Methods:

  • Systematic literature review of deep learning applications in chest X-ray analysis.
  • Analysis of recent publications focusing on pneumonia and COVID-19 detection.
  • Exploration of publicly available datasets and deep learning methodologies.

Main Results:

  • Identification of current deep learning-based solutions for chest X-ray diagnosis.
  • Analysis of emerging trends and popular techniques in the domain.
  • Compilation of relevant datasets and guidance on deep learning processes.

Conclusions:

  • Deep learning offers powerful tools for analyzing chest X-rays, improving diagnostic accuracy for pneumonia and COVID-19.
  • The review identifies key trends, available resources, and challenges, guiding future research.
  • This work serves as a valuable resource for researchers and developers in medical imaging AI.