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Computer-aided detection in chest radiography based on artificial intelligence: a survey.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiography

Background:

  • Chest radiography is a vital diagnostic tool in medicine.
  • Automatic detection of chest diseases via radiography is a key research area.
  • Computer-aided detection (CAD) systems are increasingly important.

Purpose of the Study:

  • To conduct a comprehensive survey of CAD systems in chest radiography.
  • To focus on the application of artificial intelligence (AI) in this field.
  • To review AI-driven detection of specific chest diseases and multiple conditions.

Main Methods:

  • Survey of existing literature on AI in chest radiography CAD.
  • Description of common chest X-ray datasets.
  • Overview of image preprocessing techniques (enhancement, segmentation, bone suppression).
  • Analysis of CAD systems for pulmonary nodules, tuberculosis, and interstitial lung diseases.

Main Results:

  • AI shows significant promise in CAD for chest radiography.
  • Various datasets and preprocessing methods are utilized.
  • Specific algorithms and evaluation metrics are discussed for disease detection.
  • The study covers detection of single and multiple thoracic diseases.

Conclusions:

  • AI-based CAD systems are advancing chest disease diagnosis.
  • Challenges and future trends in AI for chest radiography are identified.
  • This review provides insights into the current state and future directions of AI in medical imaging for thoracic conditions.