ArticleJournal of personalized medicine2022
A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 83 citations in OpenAlex.
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- Automated Lesion Segmentation in Medical Imaging via Integration of nnU-Net Optimization and SAM Approach.Biomedical engineering and computational biology · 2026Article
- Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach.Sensors (Basel, Switzerland) · 2025Article
- Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.Frontiers in artificial intelligence · 2025Article
- Statistical Analysis of nnU-Net Models for Lung Nodule Segmentation.Journal of personalized medicine · 2024Article
- Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization.Sensors (Basel, Switzerland) · 2023Review
- Hybrid Diagnostic Model for Improved COVID-19 Detection in Lung Radiographs Using Deep and Traditional Features.Biomimetics (Basel, Switzerland) · 2023Article
- A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images.iScience · 2023Article
- Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.Biomedical signal processing and control · 2023Article
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.Multimedia tools and applications · 2023Article
- Article
- Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images.Biomedical signal processing and control · 2023Article
- Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN.International journal of environmental research and public health · 2022Article
- A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images.Diagnostics (Basel, Switzerland) · 2022Review
- Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion.Computers in biology and medicine · 2022Article
- A novel deep fusion strategy for COVID-19 prediction using multimodality approach.Computers & electrical engineering : an international journal · 2022Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.