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Volume 12
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Article

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

by
Hanane Allioui
1,
Mazin Abed Mohammed
2,
Narjes Benameur
3,
Belal Al-Khateeb
2,
Karrar Hameed Abdulkareem
4,
Begonya Garcia-Zapirain
5,
Robertas Damaševičius
6,* and
Rytis Maskeliūnas
6
1
Computer Sciences Department, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech 40000, Morocco
2
Computer Science Department, College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq
3
Laboratory of Biophysics and Medical Technology, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, Tunis 1006, Tunisia
4
College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
5
eVIDA Laboratory, University of Deusto, 48007 Bilbao, Spain
6
Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2022, 12(2), 309; https://doi.org/10.3390/jpm12020309
Submission received: 3 February 2022 / Revised: 14 February 2022 / Accepted: 15 February 2022 / Published: 18 February 2022
(This article belongs to the Special Issue Application of Artificial Intelligence in Personalized Medicine)
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Abstract

Currently, most mask extraction techniques are based on convolutional neural networks (CNNs). However, there are still numerous problems that mask extraction techniques need to solve. Thus, the most advanced methods to deploy artificial intelligence (AI) techniques are necessary. The use of cooperative agents in mask extraction increases the efficiency of automatic image segmentation. Hence, we introduce a new mask extraction method that is based on multi-agent deep reinforcement learning (DRL) to minimize the long-term manual mask extraction and to enhance medical image segmentation frameworks. A DRL-based method is introduced to deal with mask extraction issues. This new method utilizes a modified version of the Deep Q-Network to enable the mask detector to select masks from the image studied. Based on COVID-19 computed tomography (CT) images, we used DRL mask extraction-based techniques to extract visual features of COVID-19 infected areas and provide an accurate clinical diagnosis while optimizing the pathogenic diagnostic test and saving time. We collected CT images of different cases (normal chest CT, pneumonia, typical viral cases, and cases of COVID-19). Experimental validation achieved a precision of 97.12% with a Dice of 80.81%, a sensitivity of 79.97%, a specificity of 99.48%, a precision of 85.21%, an F1 score of 83.01%, a structural metric of 84.38%, and a mean absolute error of 0.86%. Additionally, the results of the visual segmentation clearly reflected the ground truth. The results reveal the proof of principle for using DRL to extract CT masks for an effective diagnosis of COVID-19.
Keywords: multi-agent reinforcement learning; COVID-19 segmentation; CT image; mask extraction; semantic segmentation multi-agent reinforcement learning; COVID-19 segmentation; CT image; mask extraction; semantic segmentation

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MDPI and ACS Style

Allioui, H.; Mohammed, M.A.; Benameur, N.; Al-Khateeb, B.; Abdulkareem, K.H.; Garcia-Zapirain, B.; Damaševičius, R.; Maskeliūnas, R. A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation. J. Pers. Med. 2022, 12, 309. https://doi.org/10.3390/jpm12020309

AMA Style

Allioui H, Mohammed MA, Benameur N, Al-Khateeb B, Abdulkareem KH, Garcia-Zapirain B, Damaševičius R, Maskeliūnas R. A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation. Journal of Personalized Medicine. 2022; 12(2):309. https://doi.org/10.3390/jpm12020309

Chicago/Turabian Style

Allioui, Hanane, Mazin Abed Mohammed, Narjes Benameur, Belal Al-Khateeb, Karrar Hameed Abdulkareem, Begonya Garcia-Zapirain, Robertas Damaševičius, and Rytis Maskeliūnas. 2022. "A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation" Journal of Personalized Medicine 12, no. 2: 309. https://doi.org/10.3390/jpm12020309

APA Style

Allioui, H., Mohammed, M. A., Benameur, N., Al-Khateeb, B., Abdulkareem, K. H., Garcia-Zapirain, B., Damaševičius, R., & Maskeliūnas, R. (2022). A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation. Journal of Personalized Medicine, 12(2), 309. https://doi.org/10.3390/jpm12020309

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