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Related Concept Videos

Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

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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.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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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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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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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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Related Experiment Video

Updated: Nov 3, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening.

Ankita Shelke1, Madhura Inamdar1, Vruddhi Shah1

  • 1K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, India Maharshtra 400077.

SN Computer Science
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an AI model using chest X-rays for COVID-19 diagnosis and severity assessment. The deep learning approach accurately classifies normal, pneumonia, tuberculosis, and COVID-19 cases, aiding in mass screening.

Keywords:
COVID-19Chest X-raySeverity-based classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • COVID-19 pandemic presents a global health crisis.
  • Lung fibrosis and ground-glass opacity are key indicators in chest X-rays of COVID-19 patients.
  • AI can analyze radiographic differences for infection detection.

Purpose of the Study:

  • To develop and evaluate a deep learning model for accurate COVID-19 diagnosis using chest X-rays.
  • To classify chest X-rays into four categories: normal, pneumonia, tuberculosis (TB), and COVID-19.
  • To further classify COVID-19 cases based on severity (mild, medium, severe).

Main Methods:

  • Utilized deep learning models including VGG-16, DenseNet-161, and ResNet-18 for classification tasks.
  • VGG-16 was employed for differentiating normal, pneumonia, and TB cases.
  • DenseNet-161 was used for distinguishing between normal, pneumonia, and COVID-19.
  • ResNet-18 was applied for classifying COVID-19 severity.

Main Results:

  • VGG-16 achieved a test accuracy of 95.9% for normal, pneumonia, and TB classification.
  • DenseNet-161 reached a test accuracy of 98.9% for segregating normal, pneumonia, and COVID-19.
  • ResNet-18 demonstrated up to 76% accuracy in classifying COVID-19 severity.

Conclusions:

  • The proposed AI model effectively diagnoses COVID-19 from chest X-rays.
  • The system can differentiate between various lung conditions and assess COVID-19 severity.
  • This approach facilitates mass screening and primary validation for COVID-19 using X-ray imaging.