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Related Concept Videos

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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A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images.

Mojtaba Mohammadpoor1, Mehran Sheikhi Karizaki2, Mina Sheikhi Karizaki3

  • 1Electrical and Computer Department, University of Gonabad, Gonabad, Iran.

Peerj. Computer Science
|April 9, 2021
PubMed
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This study introduces a deep learning model for rapid COVID-19 screening using CT scans. The AI model accurately distinguishes positive cases, offering a fast alternative to traditional testing.

Keywords:
COVID-19 detectionCT-scanConvolutional neural networks (CNN)Deep learning

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic necessitated rapid diagnostic solutions.
  • Traditional laboratory testing for COVID-19 is time-consuming.
  • Lung CT-scans are utilized for COVID-19 detection.

Purpose of the Study:

  • To propose a deep learning neural network model for fast COVID-19 screening.
  • To evaluate the model's effectiveness in analyzing CT scans for COVID-19 detection.

Main Methods:

  • A deep learning neural network model was developed.
  • The model analyzes lung CT-scans to identify COVID-19 cases.
  • The model was applied to a publicly available dataset of positive and negative cases.

Main Results:

  • The model demonstrated the ability to distinguish between COVID-19 positive and negative cases from CT images.
  • Performance was evaluated by analyzing the impact of different parameters.
  • The model achieved over 95% accuracy and 90% ROC-AUC on random train/test data without preprocessing.

Conclusions:

  • The proposed deep learning model serves as a fast and effective screening tool for COVID-19 detection using CT scans.
  • The model's high accuracy and AUC suggest its potential as a valuable diagnostic aid.