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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Deep learning for report generation on chest X-ray images.

Mohammed Yasser Ouis1, Moulay A Akhloufi1

  • 1Perception, Robotics and Intelligent Machines Lab(PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1C 3E9, Canada.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|December 22, 2023
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Deep learning enhances chest X-ray analysis for early disease detection. This review explores its impact on computer-aided diagnosis and automated radiology report generation, improving accuracy and efficiency.

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Area of Science:

  • Medical imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Chest X-ray analysis is vital for early disease detection and screening.
  • Deep learning, particularly Convolutional Neural Networks (CNNs), offers advanced capabilities for medical image analysis.
  • Computer-aided diagnosis (CAD) systems leverage AI to assist in interpreting medical images.

Purpose of the Study:

  • To review the significance of chest X-ray analysis in healthcare.
  • To explore the impact and advancements of deep learning techniques in this field.
  • To highlight challenges and future directions in AI-driven radiology report generation.

Main Methods:

  • Review of existing literature on deep learning applications in chest X-ray analysis.
  • Focus on techniques for classification, detection, segmentation, and report generation.
  • Analysis of the role of labeled medical image databases.

Main Results:

  • Deep learning shows potential to automate and improve accuracy in chest X-ray interpretation.
  • Significant progress has been made in using AI for tasks like abnormality detection and classification.
  • AI-powered radiology report generation is a promising area with potential to aid radiologists.

Conclusions:

  • Deep learning significantly advances chest X-ray image analysis for medical diagnosis.
  • Automated report generation using AI can enhance efficiency and reduce radiologist workload.
  • Further research into deep learning holds promise for improving patient care through medical imaging.