ReviewAnnals of biomedical engineering2022
Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models.
Review in Annals of biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advancements in Image-Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review.BioMed research international · 2025Pooled it
- Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Utilizing artificial intelligence as an arbitrary tool in managing difficult COVID-19 cases in critical care medicine.World journal of critical care medicine · 2025Review
- Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods.Scientific reports · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- A comparison of CXR-CAD software to radiologists in identifying COVID-19 in individuals evaluated for Sars CoV-2 infection in Malawi and Zambia.PLOS digital health · 2025Article
- Integrating ensemble and machine learning models for early prediction of pneumonia mortality using laboratory tests.Heliyon · 2024Article
- [Dielectric properties of tidal volume changes in rabbit lung tissue in the 100 MHz~1 GHz band].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Article
- A Promising Start and Not a Panacea: ChatGPT's Early Impact and Potential in Medical Science and Biomedical Engineering Research.Annals of biomedical engineering · 2024Article
- Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset.Journal of imaging · 2023Article
- COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19 lateral flow test image classification using deep CNN and StyleGAN2.Frontiers in artificial intelligence · 2023Article
- Publications About COVID-19 Research by the BME Community.Annals of biomedical engineering · 2022Article
- An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics.Healthcare analytics (New York, N.Y.) · 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
- Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques.International journal of biomedical imaging · 2022Article
- Prediction Model of Residual Neural Network for Pathological Confirmed Lymph Node Metastasis of Ovarian Cancer.BioMed research international · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Coronavirus 2019 (COVID-19) is a highly transmissible and pathogenic virus caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in Wuhan, China, and has since spread in the whole world. This pathology has caused a major health crisis in the world. However, the early detection of this anomaly is a key task to minimize their spread. Artificial intelligence is one of the approaches commonly used by researchers to discover the problems it causes and provide solutions. These estimates would help enable health systems to take the necessary steps to diagnose and track cases of COVID. In this review, we intend to offer a novel method of automatic detection of COVID-19 using tomographic images (CT) and radiographic images (Chest X-ray). In order to improve the performance of the detection system for this outbreak, we used two deep learning models: the VGG and ResNet. The results of the experiments show that our proposed models achieved the best accuracy of 99.35 and 96.77% respectively for VGG19 and ResNet50 with all the chest X-ray 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.