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Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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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Updated: Oct 5, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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COVID-19 detection in CT and CXR images using deep learning models.

Ines Chouat1,2, Amira Echtioui3, Rafik Khemakhem1,2

  • 1ATMS Lab, Advanced Technologies for Medicine and Signals, ENIS, Sfax University, Sfax, Tunisia.

Biogerontology
|January 22, 2022
PubMed
Summary
This summary is machine-generated.

Deep transfer learning models effectively detect COVID-19 from CT scans and X-rays. VGGNet-19 and Xception models show high accuracy, aiding rapid diagnosis when RT-PCR is limited.

Keywords:
COVID-19CT imageDeep learningMedical imagingX-ray

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • Infectious diseases like COVID-19 pose significant global health risks.
  • Rapid and accurate diagnosis is crucial for managing pandemics.
  • Medical imaging, including CT scans and Chest X-rays (CXR), offers a viable diagnostic approach.

Purpose of the Study:

  • To investigate the efficacy of deep transfer learning models for detecting COVID-19.
  • To evaluate the performance of various pre-trained deep neural networks using CT and CXR images.
  • To assess the impact of data augmentation on model generalization.

Main Methods:

  • Utilized deep transfer learning with pre-trained models: ResNet50, InceptionV3, VGGNet-19, and Xception.
  • Employed data augmentation to expand the training dataset and prevent overfitting.
  • Evaluated model performance on separate CT and CXR image datasets, as well as combined modalities.

Main Results:

  • VGGNet-19 achieved 87% accuracy on CT scans, while Xception reached 98% accuracy on CXR images.
  • Combined analysis of CT and X-ray data using VGG-19 yielded 90.5% accuracy.
  • Deep learning models demonstrated strong performance in identifying COVID-19 positive cases.

Conclusions:

  • Deep transfer learning is a powerful tool for automated COVID-19 detection using medical imaging.
  • The study highlights the potential of AI in enhancing diagnostic capabilities, especially in resource-limited settings.
  • Specific models like VGGNet-19 and Xception show promise for clinical application in COVID-19 screening.