A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation.
Hanane AlliouiMazin Abed MohammedNarjes BenameurBelal Al-KhateebKarrar Hameed AbdulkareemBegonya Garcia ZapirainRobertas DamaseviciusRytis MaskeliunasPublished in: Journal of personalized medicine (2022)
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.
Keyphrases
- deep learning
- convolutional neural network
- artificial intelligence
- computed tomography
- sars cov
- coronavirus disease
- image quality
- dual energy
- contrast enhanced
- machine learning
- big data
- positron emission tomography
- positive airway pressure
- magnetic resonance imaging
- respiratory syndrome coronavirus
- healthcare
- oxidative stress
- mental health
- gene expression
- intensive care unit
- community acquired pneumonia
- respiratory failure