ArticleSLAS technology2022
COVID-19 detection using chest X-ray images based on a developed deep neural network.
Article in SLAS technology, 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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- 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
- GLAPAL-H: Global, Local, and Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.IEEE transactions on bio-medical engineering · 2026Article
- GLAPAL-H: Global, Local, And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.medRxiv : the preprint server for health sciences · 2025Article
- A multi-scale CNN with atrous spatial pyramid pooling for enhanced chest-based disease detection.PeerJ. Computer science · 2025Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm.Medical & biological engineering & computing · 2024Article
- Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification.Journal of imaging · 2023Article
- PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer.Medical & biological engineering & computing · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans.Multimedia tools and applications · 2023Article
- DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net.New generation computing · 2023Article
- Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography.Journal of imaging · 2022Article
- SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19.Biocybernetics and biomedical engineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimCurrently, a new coronavirus called COVID-19 is the biggest challenge of the human at 21st century. Now, the spread of this virus is such that mortality has risen strongly in all cities of countries. Therefore, it is necessary to think of a solution to handle the disease by fast and timely diagnosis. This paper proposes a method that uses chest X-ray imagery to divide 2-4 classes into 7 different Scenarios, including Bacterial, Viral, Healthy, and COVID-19 classes. The aim of this study is to propose a method that uses chest X-ray imagery to divide 2-4 classes into 7 different Scenarios, including Bacterial, Viral, Healthy, and COVID-19 classes.
methods6 different databases from chest X-ray imagery that have been widely used in recent studies have been gathered for this aim. A Convolutional Neural Network-Long Short Time Memory model is designed and developed to extract features from raw data hierarchically. In order to make more realistic assumptions and use the Proposed Method in the practical field, white Gaussian noise is added to the raw chest X-ray imagery. Additionally, the proposed network is tested and investigated not only on 6 expressed databases but also on two additional databases.
resultsOn the test set, the proposed network achieved an accuracy of more than 90% for all Scenarios excluding Scenario V, i.e. Healthy against the COVID-19 against the Viral, and also achieved 99% accuracy for separating the COVID-19 from the Healthy group. The results showed that the proposed network is robust to noise up to 1 dB. It is worth noting that the proposed network for two additional databases, which were only used as test databases, also achieved more than 90% accuracy. In addition, in comparison to the state-of-the-art pneumonia detection approaches, the final results obtained from the proposed network is so promising.
conclusionsThe proposed network is effective in detecting COVID-19 and other lung infectious diseases using chest X-ray imagery and can thus assist radiologists in making rapid and accurate detections.
Indexed as
Identifiers
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.