ArticleExpert systems with applications2021
Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble.
Article in Expert systems with applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 5 of them syntheses that pooled it.
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Who cites it
98 citing papers in PubMed, 5 syntheses or guidelines pooled it, 279 citations in OpenAlex.
- A systematic literature review: exploring the challenges of ensemble model for medical imaging.BMC medical imaging · 2025Pooled it
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Pneumonia Detection in Veterinary X-Rays Utilizing PaddleSeg-Based Semantic Segmentation and Mamba-Powered Classification.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2026Article
- Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.Biosensors · 2026Review
- Deep-kidney: an effective deep learning framework for chronic kidney disease prediction.Health information science and systems · 2024Article
- A multistage framework for respiratory disease detection and assessing severity in chest X-ray images.Scientific reports · 2024Article
- Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters.BMC pulmonary medicine · 2024Article
- Generalizable disease detection using model ensemble on chest X-ray images.Scientific reports · 2024Article
- An efficient multi-class classification of skin cancer using optimized vision transformer.Medical & biological engineering & computing · 2024Article
- COVID-19 infection segmentation using hybrid deep learning and image processing techniques.Scientific reports · 2023Article
- Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.Expert systems with applications · 2023Article
- Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach.Scientific reports · 2023Article
- COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.Journal of digital imaging · 2023Article
- Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.Biocybernetics and biomedical engineering · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
- Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks.BMC medical imaging · 2023Article
- Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.Scientific reports · 2023Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the detection of such visual responses associated with SARS-COV-2 infection. However, the limited availability of expert radiologists to interpret the CXR images and subtle appearance of disease radiographic responses remains the biggest bottlenecks in manual diagnosis. In this study, we present an automatic COVID screening (ACoS) system that uses radiomic texture descriptors extracted from CXR images to identify the normal, suspected, and nCOVID-19 infected patients. The proposed system uses two-phase classification approach (normal vs. abnormal and nCOVID-19 vs. pneumonia) using majority vote based classifier ensemble of five benchmark supervised classification algorithms. The training-testing and validation of the ACoS system are performed using 2088 (696 normal, 696 pneumonia and 696 nCOVID-19) and 258 (86 images of each category) CXR images, respectively. The obtained validation results for phase-I (accuracy (ACC) = 98.062%, area under curve (AUC) = 0.956) and phase-II (ACC = 91.329% and AUC = 0.831) show the promising performance of the proposed system. Further, the Friedman post-hoc multiple comparisons and z-test statistics reveals that the results of ACoS system are statistically significant. Finally, the obtained performance is compared with the existing state-of-the-art methods.
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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.