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Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

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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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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Imaging Studies for Cardiovascular System III: X-Ray01:20

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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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Imaging Studies I: CT and MRI01:14

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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X-ray Imaging01:24

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Updated: Sep 27, 2025

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Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models.

Wassim Zouch1, Dhouha Sagga2,3, Amira Echtioui2

  • 1King Abdulaziz University (KAU), Jeddah, Saudi Arabia. wzouch@kau.edu.sa.

Annals of Biomedical Engineering
|April 13, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-driven method for early COVID-19 detection using chest X-rays. Deep learning models like VGG19 and ResNet50 achieved high accuracy, aiding healthcare systems in diagnosing and tracking the virus.

Keywords:
COVID-19CTChest X-rayConvolutional neural networkDeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • Coronavirus 2019 (COVID-19), caused by SARS-CoV-2, is a global health crisis requiring rapid detection.
  • Early identification of COVID-19 is crucial for effective containment and management.
  • Artificial intelligence (AI) offers promising avenues for developing advanced diagnostic tools.

Purpose of the Study:

  • To present a novel automated method for detecting COVID-19 using medical imaging.
  • To evaluate the efficacy of deep learning models for COVID-19 diagnosis from radiographic images.
  • To enhance the performance of diagnostic systems for infectious respiratory diseases.

Main Methods:

  • Utilized deep learning models, specifically VGG19 and ResNet50, for image analysis.
  • Applied the models to a dataset of chest X-ray images for COVID-19 detection.
  • Compared the performance of the VGG and ResNet architectures in identifying SARS-CoV-2 infections.

Main Results:

  • The VGG19 model achieved a high accuracy of 99.35% in detecting COVID-19 from chest X-rays.
  • The ResNet50 model demonstrated strong performance with an accuracy of 96.77%.
  • Both deep learning models proved effective in the automated detection of COVID-19.

Conclusions:

  • Deep learning models, particularly VGG19 and ResNet50, show significant potential for accurate and rapid COVID-19 detection.
  • AI-powered analysis of chest X-rays can support healthcare systems in diagnosing and managing the pandemic.
  • This approach offers a valuable tool for early anomaly detection and disease surveillance.