ArticleRadiography (London, England : 1995)2022
Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost.
Article in Radiography (London, England : 1995), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 6 citations in OpenAlex.
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- In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.Journal of imaging informatics in medicine · 2025Article
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Concatenated CNN-Based Pneumonia Detection Using a Fuzzy-Enhanced Dataset.Sensors (Basel, Switzerland) · 2024Article
- Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study.Research (Washington, D.C.) · 2024Article
- Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome.PeerJ. Computer science · 2024Article
- A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors.Diagnostics (Basel, Switzerland) · 2023Article
- Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- An innovative ensemble model based on deep learning for predicting COVID-19 infection.Scientific reports · 2023Article
- Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods.Diagnostics (Basel, Switzerland) · 2023Article
- A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.Diagnostics (Basel, Switzerland) · 2023Article
- Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models.Diagnostics (Basel, Switzerland) · 2023Article
- Review on chest pathogies detection systems using deep learning techniques.Artificial intelligence review · 2023Article
- Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier.Healthcare (Basel, Switzerland) · 2023Article
- 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
- Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.Quantitative imaging in medicine and surgery · 2023Article
- Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?Diagnostics (Basel, Switzerland) · 2023Review
- Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net.New generation computing · 2023Article
- CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIn late 2019 and after the COVID-19 pandemic in the world, many researchers and scholars tried to provide methods for detecting COVID-19 cases. Accordingly, this study focused on identifying patients with COVID-19 from chest X-ray images.
methodsIn this paper, a method for diagnosing coronavirus disease from X-ray images was developed. In this method, DenseNet169 Deep Neural Network (DNN) was used to extract the features of X-ray images taken from the patients' chests. The extracted features were then given as input to the Extreme Gradient Boosting (XGBoost) algorithm to perform the classification task.
resultsEvaluation of the proposed approach and its comparison with the methods presented in recent years revealed that this method was more accurate and faster than the existing ones and had an acceptable performance for detecting COVID-19 cases from X-ray images. The experiments showed 98.23% and 89.70% accuracy, 99.78% and 100% specificity, 92.08% and 95.20% sensitivity in two and three-class problems, respectively.
conclusionThis study aimed to detect people with COVID-19, focusing on non-clinical approaches. The developed method could be employed as an initial detection tool to assist the radiologists in more accurate and faster diagnosing the disease. IMPLICATION FOR PRACTICE: The proposed method's simple implementation, along with its acceptable accuracy, allows it to be used in COVID-19 diagnosis. Moreover, the gradient-based class activation mapping (Grad-CAM) can be used to represent the deep neural network's decision area on a heatmap. Radiologists might use this heatmap to evaluate the chest area more accurately.
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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.