ArticleJournal of X-ray science and technology2020
Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.
Article in Journal of X-ray science and technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 2 of them syntheses that pooled it.
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Who cites it
102 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence for COVID-19: A Systematic Review.Frontiers in medicine · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.BioMed research international · 2022Trial
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- Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model.Scientific reports · 2025Article
- Enhancing Clinical Insights: New Radiographic Grading for Lumbar Facet Joint Degeneration, A Reliability Study.JOR spine · 2025Article
- Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data.Journal of imaging · 2025Article
- Prediction of TP53 mutations across female reproductive system pan-cancers using deep multimodal PET/CT radiogenomics.Frontiers in medicine · 2025Article
- Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Artificial intelligence performance in testing microfluidics for point-of-care.Lab on a chip · 2024Article
- Optimization of vision transformer-based detection of lung diseases from chest X-ray images.BMC medical informatics and decision making · 2024Article
- Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network.Diagnostics (Basel, Switzerland) · 2024Article
- Evaluation and analysis of surgical treatment for single-level or multi-level lumbar degenerative disease based on radiography.Quantitative imaging in medicine and surgery · 2024Article
- A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.Frontiers in medicine · 2024Article
- CovC-ReDRNet: A Deep Learning Model for COVID-19 Classification.Machine learning and knowledge extraction · 2023Article
- Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling.Computers in biology and medicine · 2023Article
- Simultaneous Super-Resolution and Classification of Lung Disease Scans.Diagnostics (Basel, Switzerland) · 2023Article
- Biases associated with database structure for COVID-19 detection in X-ray images.Scientific reports · 2023Article
42 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system.
objectiveOne of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images.
methodsDue to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task.
resultsA total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively.
conclusionThis study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.
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