ReviewComputers in biology and medicine2022
COVID-19 image classification using deep learning: Advances, challenges and opportunities.
Review in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis 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.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 151 citations in OpenAlex.
- Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Histopathology-Based Prostate Cancer Classification Using ResNet: A Comprehensive Deep Learning Analysis.Journal of imaging informatics in medicine · 2026Article
- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Recent feeding history drives prediction of daily feed intake in lactating sows.Translational animal science · 2026Article
- AI-driven drug reposition for pathogens: a new paradigm in pandemic preparedness.Frontiers in chemistry · 2026Review
- Application of Generative Adversarial Networks to Improve COVID-19 Classification on Ultrasound Images.Journal of imaging · 2025Article
- Editorial for the Special Issue "Advances in Medical Image Processing, Segmentation, and Classification".Diagnostics (Basel, Switzerland) · 2025Article
- Adaptive dual-window enhancement and multi-scale texture prior fusion for robust kidney CT classification.PloS one · 2025Article
- Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation.New microbes and new infections · 2024Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics.Biomimetics (Basel, Switzerland) · 2024Article
- Omni-dimensional dynamic convolution feature coordinate attention network for pneumonia classification.Visual computing for industry, biomedicine, and art · 2024Article
- An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.Bioengineering (Basel, Switzerland) · 2024Article
- COVID-19 PNEUMONIA CHEST X-RAY PATTERN SYNTHESIS BY STABLE DIFFUSION.Proceedings. IEEE Southwest Symposium on Image Analysis and Interpretation · 2024Article
- Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection.PloS one · 2024Article
- Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.Scientific reports · 2023Article
- MERGE: A model for multi-input biomedical federated learning.Patterns (New York, N.Y.) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.
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What OpenQuestion holds
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.