ReviewComputational and mathematical methods in medicine2021
An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19.
Review in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- CNN Sample-Size Effects Across Biomedical Datasets: A Reliability Pattern in Overfitting, Ranking, and Monotonicity.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- Work experiences and practices of radiographers during the coronavirus disease 2019 pandemic: Future pandemic preparation.Health SA = SA Gesondheid · 2026Article
- Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT.Tomography (Ann Arbor, Mich.) · 2024Article
- Augmenting Radiological Diagnostics with AI for Tuberculosis and COVID-19 Disease Detection: Deep Learning Detection of Chest Radiographs.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images.Frontiers in public health · 2024Article
- PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer.Medical & biological engineering & computing · 2023Article
- Fully Automatic Left Ventricle Segmentation Using Bilateral Lightweight Deep Neural Network.Life (Basel, Switzerland) · 2023Article
- DCML: Deep contrastive mutual learning for COVID-19 recognition.Biomedical signal processing and control · 2022Article
- Advances in diagnostic tools for respiratory tract infections: from tuberculosis to COVID-19 - changing paradigms?ERJ open research · 2022Review
- Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection.Computers in biology and medicine · 2022Article
- Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?BMC medical research methodology · 2022Article
- Indirect supervision applied to COVID-19 and pneumonia classification.Informatics in medicine unlocked · 2022Article
- Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm.Entropy (Basel, Switzerland) · 2021Article
- Malaysian Nurses' Knowledge of Radiation Protection: A Cross-Sectional Study.Radiology research and practice · 2021Article
- SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.
Indexed as
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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.