ArticleExpert systems2022
Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.
Article in Expert systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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
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Who cites it
66 citing papers in PubMed, 2 syntheses or guidelines pooled it, 164 citations in OpenAlex.
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review.International journal of environmental research and public health · 2022Pooled it
- Artificial intelligence risk prediction model for common respiratory pathogens in China based on heterogeneous multi-source clinical and geographic data: A modeling study.PLOS digital health · 2026Article
- A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.Scientific reports · 2025Article
- Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Estimation of unconfirmed COVID-19 cases from a cross-sectional survey of >10 000 households and a symptom-based machine learning model in Gilgit-Baltistan, Pakistan.BMJ public health · 2025Article
- Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model.Journal of imaging informatics in medicine · 2024Article
- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
- COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features.Heliyon · 2024Article
- A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry.Frontiers in big data · 2024Article
- Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms.Viruses · 2023Article
- Emerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectives.Biomedical signal processing and control · 2023Review
- 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 Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms.Mobile networks and applications : MONET · 2023Article
- Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images.Intelligent systems with applications · 2023Article
- COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm.Cluster computing · 2023Article
- DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.Sensors (Basel, Switzerland) · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 10 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
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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.