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A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation.

Hanane Allioui1, Mazin Abed Mohammed2, Narjes Benameur3

  • 1Computer Sciences Department, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech 40000, Morocco.

Journal of Personalized Medicine
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-agent deep reinforcement learning (DRL) method for efficient mask extraction in medical imaging. The DRL approach enhances COVID-19 diagnosis from CT scans by improving segmentation accuracy and reducing manual effort.

Keywords:
COVID-19 segmentationCT imagemask extractionmulti-agent reinforcement learningsemantic segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Current mask extraction methods, often CNN-based, face limitations in medical image segmentation.
  • Advanced artificial intelligence (AI) techniques are needed to overcome these challenges.
  • Cooperative agents can improve the efficiency of automatic image segmentation.

Purpose of the Study:

  • To introduce a new mask extraction method using multi-agent deep reinforcement learning (DRL).
  • To minimize manual mask extraction time and enhance medical image segmentation.
  • To improve the accuracy of COVID-19 diagnosis using CT images.

Main Methods:

  • A modified Deep Q-Network within a multi-agent DRL framework was developed for mask detection.
  • The method was applied to COVID-19 computed tomography (CT) images, including normal, pneumonia, and viral cases.
  • Visual features of COVID-19 infected areas were extracted for diagnosis.

Main Results:

  • The DRL method achieved high performance metrics: 97.12% precision, 80.81% Dice, 79.97% sensitivity, 99.48% specificity, 85.21% precision, 83.01% F1 score, 84.38% structural metric, and 0.86% mean absolute error.
  • Visual segmentation results closely matched the ground truth.
  • The method demonstrated proof of principle for DRL in CT mask extraction for COVID-19 diagnosis.

Conclusions:

  • Multi-agent DRL offers an effective approach for mask extraction in medical imaging.
  • This DRL-based technique enhances COVID-19 diagnosis accuracy and efficiency from CT scans.
  • The method shows promise for optimizing diagnostic tests and saving clinical time.