ArticleCognitive computation2022
Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network.
Article in Cognitive computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.BioMed research international · 2022Pooled it
- Quantum machine learning for predicting anastomotic leak: a clinical study.Scientific reports · 2026Article
- Signal transmission optimization of intelligent metasurface-assisted communication system based on improved DQNN.Scientific reports · 2026Article
- Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data.Scientific reports · 2025Article
- Future Potential of Quantum Computing and Simulations in Biological Science.Molecular biotechnology · 2024Review
- RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing.Diagnostics (Basel, Switzerland) · 2024Article
- Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment.Cureus · 2024Review
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Revolutionizing heart disease prediction with quantum-enhanced machine learning.Scientific reports · 2024Article
- FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease.Internet of things (Amsterdam, Netherlands) · 2023Article
- Self-paced Multi-view Learning for CT-based severity assessment of COVID-19.Biomedical signal processing and control · 2023Article
- DDiagnostics (Basel, Switzerland) · 2022Article
- COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images.Healthcare (Basel, Switzerland) · 2022Article
- Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review.JMIR medical informatics · 2022Article
- RETRACTED: Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- Investigating Factors Influencing Nurses' Behavioral Intention to Use Mobile Learning: Using a Modified Unified Theory of Acceptance and Use of Technology Model.Frontiers in psychology · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: COVID-19 is a novel virus that affects the upper respiratory tract, as well as the lungs. The scale of the global COVID-19 pandemic, its spreading rate, and deaths are increasing regularly. Computed tomography (CT) scans can be used carefully to detect and analyze COVID-19 cases. In CT images/scans, ground-glass opacity (GGO) is found in the early stages of infection. While in later stages, there is a superimposed pulmonary consolidation. Methods: This research investigates the quantum machine learning (QML) and classical machine learning (CML) approaches for the analysis of COVID-19 images. The recent developments in quantum computing have led researchers to explore new ideas and approaches using QML. The proposed approach consists of two phases: in phase I, synthetic CT images are generated through the conditional adversarial network (CGAN) to increase the size of the dataset for accurate training and testing. In phase II, the classification of COVID-19/healthy images is performed, in which two models are proposed: CML and QML. Result: The proposed model achieved 0.94 precision (Pn), 0.94 accuracy (Ac), 0.94 recall (Rl), and 0.94 F1-score (Fe) on POF Hospital dataset while 0.96 Pn, 0.96 Ac, 0.95 Rl, and 0.96 Fe on UCSD-AI4H dataset. Conclusion: The proposed method achieved better results when compared to the latest published work in this domain.
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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.