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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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Effective hybrid deep learning model for COVID-19 patterns identification using CT images.

Dheyaa Ahmed Ibrahim1, Dilovan Asaad Zebari2, Hussam J Mohammed3

  • 1Communications Engineering Techniques Department, Information Technology Collage Imam Ja'afar Al-Sadiq University Baghdad Iraq.

Expert Systems
|August 9, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a hybrid deep learning system to accurately identify COVID-19 from lung CT scans. The model achieved 95% accuracy, offering a valuable tool for clinical diagnosis.

Keywords:
COVID‐19 identificationCT scan imagesdeep learning modelsfeature fusion

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Coronavirus disease 2019 (COVID-19) remains a global health concern, necessitating advanced diagnostic methods.
  • Lung computed tomography (CT) scans are crucial for COVID-19 diagnosis, complementing nucleic acid tests.
  • Deep learning (DL) offers potential for automated analysis of medical images.

Purpose of the Study:

  • To investigate the efficacy of hybrid deep learning methods for rapid and accurate COVID-19 identification using lung CT images.
  • To propose a novel system for COVID-19 prediction, integrating lung segmentation and feature extraction.
  • To enhance diagnostic capabilities for COVID-19 through an AI-driven approach.

Main Methods:

  • A hybrid deep learning system was developed, starting with lung CT image segmentation.
  • Lung segmentation utilized a no-threshold histogram-based method followed by the GrabCut technique.
  • Three pre-trained DL models (VGGNet, C-DBN, HRNet) were combined for feature extraction and prediction.

Main Results:

  • The proposed hybrid DL model achieved a 95% accuracy rate on the COVID-19 CT dataset.
  • The model demonstrated superior performance compared to several existing state-of-the-art methods.
  • Effective screening of COVID-19 CT images was confirmed by the model's performance.

Conclusions:

  • The developed hybrid deep learning system shows significant potential as an assistive diagnostic tool for COVID-19.
  • The model's accuracy and speed in analyzing lung CT scans can aid clinical professionals.
  • This AI-driven approach can improve the early detection and management of COVID-19.