ArticleInterdisciplinary sciences, computational life sciences2020
COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.
Article in Interdisciplinary sciences, computational life sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling.Computers in biology and medicine · 2023Article
- In Search of an Efficient and Reliable Deep Learning Model for Identification of COVID-19 Infection from Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans.Intelligent systems with applications · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- FWLICM-Deep Learning: Fuzzy Weighted Local Information C-Means Clustering-Based Lung Lobe Segmentation with Deep Learning for COVID-19 Detection.Journal of digital imaging · 2022Article
- COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer.Medical & biological engineering & computing · 2022Article
- Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias?Medical physics · 2022Article
- Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays.Neural computing & applications · 2022Article
- EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images.Scientific reports · 2021Article
- Enhance Portable Radiograph for Fast and High Accurate COVID-19 Monitoring.Diagnostics (Basel, Switzerland) · 2021Article
- Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.Interdisciplinary sciences, computational life sciences · 2021Article
- Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data.Medical & biological engineering & computing · 2021Article
- Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson's disease.Neural computing & applications · 2021Article
- TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19.Biocybernetics and biomedical engineeringArticle
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce pulmonary failures, and chest X-ray imaging becomes one of the major confirmed diagnostic technologies. The very limited number of publicly available samples has rendered the training of the deep neural networks unstable and inaccurate. This study proposed a two-step transfer learning pipeline and a deep residual network framework COVID19XrayNet for the COVID-19 detection problem based on chest X-ray images. COVID19XrayNet firstly tunes the transferred model on a large dataset of chest X-ray images, which is further tuned using a small dataset of annotated chest X-ray images. The final model achieved 0.9108 accuracy. The experimental data also suggested that the model may be improved with more training samples being released. COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.
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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.