ArticleComputational and mathematical methods in medicine2022
Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review.JMIR mHealth and uHealth · 2024Pooled it
- NextGen lung disease diagnosis with explainable artificial intelligence.Scientific reports · 2025Article
- Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection.Journal of clinical medicine · 2025Article
- Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.Heliyon · 2024Article
- Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data.BMC medical informatics and decision making · 2024Article
- Improving a Deep Learning Model to Accurately Diagnose LVNC.Journal of clinical medicine · 2023Article
- Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants.Diagnostics (Basel, Switzerland) · 2023Article
- A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI).Journal of clinical medicine · 2023Article
- COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images.BMC medical imaging · 2023Article
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms.Journal of healthcare engineering · 2022Article
- Automatic Detection of Horner Syndrome by Using Facial Images.Journal of healthcare engineering · 2022Article
- COVID-19 Data Analytics Using Extended Convolutional Technique.Interdisciplinary perspectives on infectious diseases · 2022Article
- Role of mobile health applications in prevention and detection of pandemic disease: A population perspective.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: While the COVID-19 pandemic was waning in most parts of the world, a new wave of COVID-19 Omicron and Delta variants in Central Asia and the Middle East caused a devastating crisis and collapse of health-care systems. As the diagnostic methods for this COVID-19 variant became more complex, health-care centers faced a dramatic increase in patients. Thus, the need for less expensive and faster diagnostic methods led researchers and specialists to work on improving diagnostic testing. Method: Inspired by the COVID-19 diagnosis methods, the latest and most efficient deep learning algorithms in the field of extracting X-ray and CT scan image features were used to identify COVID-19 in the early stages of the disease. Results: We presented a general framework consisting of two models which are developed by convolutional neural network (CNN) using the concept of transfer learning and parameter optimization. The proposed phase of the framework was evaluated on the test dataset and yielded remarkable results and achieved a detection sensitivity, specificity, and accuracy of 0.99, 0.986, and 0.988, for the first phase and 0.997, 0.9976, and 0.997 for the second phase, respectively. In all cases, the whole framework was able to successfully classify COVID-19 and non-COVID-19 cases from CT scans and X-ray images. Conclusion: Since the proposed framework was based on two deep learning models that used two radiology modalities, it was able to significantly assist radiologists in detecting COVID-19 in the early stages. The use of models with this feature can be considered as a powerful and reliable tool, compared to the previous models used in the past pandemics.
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