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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
08:05

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A quantum-clustering optimization method for COVID-19 CT scan image segmentation.

Pritpal Singh1, Surya Sekhar Bose2

  • 1Institute of Theoretical Physics, Jagiellonian University, ul.Łojasiewicza 11, Kraków 30-348, Poland.

Expert Systems with Applications
|August 2, 2021
PubMed
Summary
This summary is machine-generated.

A new FFQOAK method accurately detects COVID-19 pneumonia in chest CT scans. This novel image segmentation technique enhances early diagnosis, improving patient outcomes for Coronavirus Disease 2019.

Keywords:
Computed tomography (CT) imagesCoronavirus Disease 2019 (COVID-19)Fast forward quantum optimization algorithm (FFQOA)Image segmentationK-means clustering (KMC) algorithm

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Coronavirus Disease 2019 (COVID-19) poses a significant global health challenge.
  • Early and accurate diagnosis of COVID-19 pneumonia is crucial for effective patient management.
  • Existing diagnostic methods require timely enhancements for rapid screening.

Purpose of the Study:

  • To introduce an innovative early screening method for COVID-19 pneumonia using chest CT scans.
  • To develop a novel image segmentation technique for precise identification of infected regions.
  • To improve the accuracy and efficiency of COVID-19 diagnosis.

Main Methods:

  • A new image segmentation method, FFQOAK, combining K-means clustering algorithm (KMC) and a fast forward quantum optimization algorithm (FFQOA).
  • The FFQOAK method clusters gray level values using KMC and optimizes segmentation with FFQOA.
  • Validation performed on diverse chest CT scan images from COVID-19 patients.

Main Results:

  • The FFQOAK method demonstrated high performance in segmenting chest CT images.
  • Achieved a Jaccard similarity coefficient of 0.90 and a correlation coefficient of 0.91 across experimental sets.
  • Outperformed existing benchmark image segmentation methods in accuracy and effectiveness.

Conclusions:

  • The FFQOAK method offers a promising approach for the early and accurate detection of COVID-19 pneumonia.
  • This AI-driven image segmentation technique can significantly aid medical professionals in diagnosing COVID-19.
  • The study highlights the potential of advanced computational methods in combating infectious disease outbreaks.