ArticleHealthcare (Basel, Switzerland)2022
Detection of COVID-19 Based on Chest X-rays Using Deep Learning.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Externally Validated Deep Learning Analysis of Chest Radiographs for Differentiating COVID-19 and Viral Pneumonia.Diagnostics (Basel, Switzerland) · 2026Article
- AdaptiveInvolutionNet: Spatially-adaptive involution with channel-wise attention for breast MRI tumor classification.PloS one · 2026Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Multi-Modal Machine Learning Approach for COVID-19 Detection Using Biomarkers and X-Ray Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection.Scientific reports · 2024Article
- Article
- The COVID-19 Pandemic: How Technology Is Reshaping Public Health and Medicine.Bioengineering (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
- Lung and Infection CT-Scan-Based Segmentation with 3D UNet Architecture and Its Modification.Healthcare (Basel, Switzerland) · 2023Article
- Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images.International journal of environmental research and public health · 2023Article
- Melanoma Detection Using Deep Learning-Based Classifications.Healthcare (Basel, Switzerland) · 2022Article
- Unsupervised Outlier Detection in IOT Using Deep VAE.Sensors (Basel, Switzerland) · 2022Article
- A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma.Healthcare (Basel, Switzerland) · 2022Article
- Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques.International journal of biomedical imaging · 2022Article
- SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19.Biocybernetics and biomedical engineeringArticle
- Article
- COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network.Biocybernetics and biomedical engineeringArticle
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Authors and funding
4 authors.
Funding
Abstract
The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease's spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. Two different DL approaches based on a pertained neural network model (ResNet-50) for COVID-19 detection using chest X-ray (CXR) images are proposed in this study. Augmenting, enhancing, normalizing, and resizing CXR images to a fixed size are all part of the preprocessing stage. This research proposes a DL method for classifying CXR images based on an ensemble employing multiple runs of a modified version of the Resnet-50. The proposed system is evaluated against two publicly available benchmark datasets that are frequently used by several researchers: COVID-19 Image Data Collection (IDC) and CXR Images (Pneumonia). The proposed system validates its dominance over existing methods such as VGG or Densnet, with values exceeding 99.63% in many metrics, such as accuracy, precision, recall, F1-score, and Area under the curve (AUC), based on the performance results obtained.
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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.