ArticleDiagnostics (Basel, Switzerland)2021
Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Ethical and Legal Concerns of Deepfake Technology in Biomedical Imaging: A Comprehensive Survey.Annals of biomedical engineering · 2026Review
- Synthetic electroretinogram signal generation using a conditional generative adversarial network.Documenta ophthalmologica. Advances in ophthalmology · 2025Article
- FairDiffusion: Enhancing equity in latent diffusion models via fair Bayesian perturbation.Science advances · 2025Article
- Synthetic data: how could it be used in infectious disease research?Future microbiology · 2024Article
- Unsupervised anomaly detection with generative adversarial networks in mammography.Scientific reports · 2023Article
- Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms.Neural computing & applications · 2023Article
- A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images.Journal of ambient intelligence and humanized computing · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Synthetic data in health care: A narrative review.PLOS digital health · 2023Article
- Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.Healthcare (Basel, Switzerland) · 2022Article
- Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.Journal of clinical medicine · 2022Review
- Binary Simulated Normal Distribution Optimizer for feature selection: Theory and application in COVID-19 datasets.Expert systems with applications · 2022Article
- Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review.JMIR medical informatics · 2022Article
- Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images.Knowledge-based systems · 2022Article
- Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model.Computational intelligence and neuroscience · 2022Article
- Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet.Frontiers in microbiology · 2022Article
- Speech as a Biomarker for COVID-19 Detection Using Machine Learning.Computational intelligence and neuroscience · 2022Article
- ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images.Multimedia tools and applications · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Most of the affected people have mild symptoms while some people develop acute respiratory distress syndrome (ARDS), which damages organs like the lungs and heart. Chest X-rays (CXRs) have been widely used to identify abnormalities that help in detecting the COVID-19 virus. They have also been used as an initial screening procedure for individuals highly suspected of being infected. However, the availability of radiographic CXRs is still scarce. This can limit the performance of deep learning (DL) based approaches for COVID-19 detection. To overcome these limitations, in this work, we developed an Auxiliary Classifier Generative Adversarial Network (ACGAN), to generate CXRs. Each generated X-ray belongs to one of the two classes COVID-19 positive or normal. To ensure the goodness of the synthetic images, we performed some experimentation on the obtained images using the latest Convolutional Neural Networks (CNNs) to detect COVID-19 in the CXRs. We fine-tuned the models and achieved more than 98% accuracy. After that, we also performed feature selection using the Harmony Search (HS) algorithm, which reduces the number of features while retaining classification accuracy. We further release a GAN-generated dataset consisting of 500 COVID-19 radiographic images.
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Registered trials
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.