ArticleEuropean radiology2021
COVID-19 classification of X-ray images using deep neural networks.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Deep Learning on Misaligned Dual-Energy Chest X-ray Images Using Paired Cycle-Consistent Generative Adversarial Networks.Journal of imaging informatics in medicine · 2026Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Retaking assessment system based on the inspiratory state of chest X-ray image.Radiological physics and technology · 2025Article
- Non-Contact Cross-Person Activity Recognition by Deep Metric Ensemble Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Pulmonary abnormality screening on chest x-rays from different machine specifications: a generalized AI-based image manipulation pipeline.European radiology experimental · 2023Article
- Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.Scientific reports · 2023Article
- Leveraging human expert image annotations to improve pneumonia differentiation through human knowledge distillation.Scientific reports · 2023Article
- Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- Classification of chest X-ray images by incorporation of medical domain knowledge into operation branch networks.BMC medical imaging · 2023Article
- Prediction of body weight from chest radiographs using deep learning with a convolutional neural network.Radiological physics and technology · 2023Article
- Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods.International journal of environmental research and public health · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography.Informatics in medicine unlocked · 2023Article
- Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision.Expert systems with applications · 2022Article
- Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection.Journal of imaging · 2022Article
- Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans.Diagnostics (Basel, Switzerland) · 2022Article
- COVID-CCD-Net: COVID-19 and colon cancer diagnosis system with optimized CNN hyperparameters using gradient-based optimizer.Medical & biological engineering & computing · 2022Article
- A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray.Diagnostics (Basel, Switzerland) · 2022Review
- Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound With Applications to COVID-19.IEEE transactions on medical imaging · 2022Article
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Authors and funding
28 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesIn the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model's results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals.
methodsIn this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image.
resultsOur model achieved accuracy of 90.3%, (95% CI: 86.3-93.7%) specificity of 90% (95% CI: 84.3-94%), and sensitivity of 90.5% (95% CI: 85-94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93-0.97).
conclusionWe provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19. KEY POINTS: • A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%. • A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model's image embeddings.
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