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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

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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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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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Related Experiment Video

Updated: Oct 26, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image.

Najmul Hasan1, Yukun Bao1, Ashadullah Shawon2

  • 1Center for Modern Information Management, School of Management, Huazhong University of Science and Technology, Wuhan, 430074 People's Republic of China.

SN Computer Science
|August 2, 2021
PubMed
Summary

Early detection of Coronavirus 2019 (COVID-19) is crucial for pandemic control. This study introduces a novel artificial intelligence method using convolutional neural networks (CNNs) for accurate COVID-19 prediction from CT scans.

Keywords:
COVID-19CT imageDeep learningDenseNet-121

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Epidemiology

Background:

  • The Coronavirus 2019 (COVID-19) pandemic has caused unprecedented global health and societal disruption.
  • Effective containment strategies rely on early and accurate detection of the virus, especially given the lack of a widely available vaccine.
  • Computed Tomography (CT) imaging is a valuable tool for diagnosing pneumonia and shows potential for COVID-19 identification.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence-based technique for predicting COVID-19 infection using CT images.
  • To leverage advanced Convolutional Neural Network (CNN) architectures for enhanced diagnostic accuracy.

Main Methods:

  • A novel approach utilizing a modified DenseNet-121 CNN architecture was employed for image analysis.
  • The model was trained and validated on CT scans to predict the presence of COVID-19.

Main Results:

  • The proposed CNN model achieved an accuracy exceeding 92%.
  • The model demonstrated a recall rate of 95%, indicating strong performance in identifying COVID-19 positive cases.
  • The results suggest the AI-driven approach is a promising auxiliary tool for COVID-19 detection.

Conclusions:

  • Artificial intelligence, specifically CNNs like DenseNet-121, can effectively predict COVID-19 from CT scans.
  • This AI-powered method offers a reliable and accurate auxiliary technique for early detection and management of COVID-19.
  • Further development and integration of such AI tools can aid in controlling the spread of infectious diseases.