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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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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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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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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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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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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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Related Experiment Video

Updated: Jul 30, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images.

Vandana Bhattacharjee1, Ankita Priya1, Nandini Kumari1,2

  • 1Birla Institute of Technology Mesra, Ranchi, 835215 India.

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|May 11, 2023
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Summary

This study introduces DeepCOVNet, a deep learning model for detecting COVID-19 from chest X-rays. The model achieved 96.77% accuracy in classifying COVID-19, Normal, and Pneumonia cases.

Keywords:
COVID-19Chest X-rayDeep learningDeepCOVNetParameter optimization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • The COVID-19 pandemic necessitates accurate diagnostic tools.
  • Differentiating COVID-19 from other respiratory conditions like pneumonia using chest X-rays (CXR) is critical.
  • Technology-enabled solutions are vital for efficient disease detection.

Purpose of the Study:

  • To propose and evaluate a deep learning model for COVID-19 detection using CXR images.
  • To provide a methodical approach for preparing data to train robust deep learning models.
  • To compare a custom-built model against pre-trained models for COVID-19 classification.

Main Methods:

  • Development of a 3-convolutional layer Deep Neural Network named "DeepCOVNet".
  • Training the model on refactored datasets combining images from multiple sources.
  • Classifying CXR images into three categories: COVID-19, Normal, and Pneumonia.

Main Results:

  • The DeepCOVNet model achieved a classification accuracy of 96.77%.
  • The model demonstrated a F1-score of 0.96 in classifying COVID-19, Normal, and Pneumonia cases.
  • Effective classification of COVID-19 patients was achieved using CXR images.

Conclusions:

  • Deep learning models, such as DeepCOVNet, are effective for classifying COVID-19 from CXR images.
  • Data preparation is a crucial step in building robust deep learning models for medical image analysis.
  • The proposed approach offers a viable technological solution for COVID-19 screening.