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Computed Tomography01:10

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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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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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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 Investigation I: X-ray and CT01:30

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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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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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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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Related Experiment Video

Updated: Sep 3, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from

Mohammed Habib1,2, Muhammad Ramzan1, Sajid Ali Khan3

  • 1Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, 11673 Riyadh, Saudi Arabia.

Journal of Grid Computing
|July 25, 2022
PubMed
Summary

This study introduces an efficient COVID-19 classification system using hybrid feature extraction from medical images. The novel approach combines deep learning and handcrafted features for improved accuracy and faster detection of COVID-19.

Keywords:
COVID-19 detectionCT scanDeep learningHandcrafted featuresMedical imagesWeber local descriptorX-ray

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • The COVID-19 pandemic has caused significant global health and economic disruption.
  • Automated medical image analysis aids in disease diagnosis and management.
  • Existing diagnostic methods require enhancement for efficiency and accuracy.

Purpose of the Study:

  • To propose a novel, efficient framework for COVID-19 classification using medical imaging.
  • To develop a hybrid feature extraction approach for enhanced diagnostic performance.
  • To improve the accuracy and speed of COVID-19 detection systems.

Main Methods:

  • A hybrid feature extraction method combining deep learning (ResNet101, DenseNet201) and handcrafted features (Weber Local Descriptor with DCT).
  • Image data preprocessing followed by feature extraction and fusion.
  • Feature selection using entropy and performance evaluation against established methods.

Main Results:

  • The proposed framework demonstrated superior performance compared to existing methods.
  • Achieved high accuracy in COVID-19 classification.
  • Showcased improved efficiency in terms of processing time.

Conclusions:

  • The developed hybrid feature extraction framework is effective for efficient COVID-19 classification.
  • This approach offers a promising tool for automated detection and diagnosis of COVID-19.
  • The system provides a valuable contribution to combating the pandemic through advanced medical imaging analysis.