ArticleAn international journal on information fusion2023
UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.
Article in An international journal on information fusion, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Uncertainty quantification-based DMEFNet for reliable modelling of heart sound signals.Scientific reports · 2026Article
- Artificial intelligence in ultrasound: divergent pathways, shared challenges.BJR artificial intelligence · 2026Review
- Identification and mechanism analysis of biomarkers related to butyrate metabolism in COVID-19 patients.Annals of medicine · 2025Article
- Uncertainty Quantification in Automated Detection of Vertebral Metastasis Using Ensemble Monte Carlo Dropout.Journal of imaging informatics in medicine · 2025Article
- Cross-domain additive learning of new knowledge rather than replacement.Biomedical engineering letters · 2024Article
- A robust ensemble deep learning framework for accurate diagnoses of tuberculosis from chest radiographs.Frontiers in medicine · 2024Article
- Uncertainty quantification in multi-class image classification using chest X-ray images of COVID-19 and pneumonia.Frontiers in artificial intelligence · 2024Article
- Multi-centre benchmarking of deep learning models for COVID-19 detection in chest x-rays.Frontiers in radiology · 2024Article
- Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.Briefings in bioinformatics · 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
- Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Early detection of tuberculosis using hybrid feature descriptors and deep learning network.Polish journal of radiology · 2023Article
- Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions.Frontiers in bioengineering and biotechnology · 2023Review
- LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.Multimedia tools and applications · 2023Article
- Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Developing a Novel Immune-Related Seven-Gene Signature and Immune Infiltration Pattern in Patients with COVID-19 and Cardiovascular Disease.Journal of cardiovascular development and disease · 2022Article
- Deep Bayesian Unsupervised Lifelong Learning.Neural networks : the official journal of the International Neural Network Society · 2022Article
- Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of the existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a new, simple but efficient deep learning feature fusion model, called
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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.