ArticleBiology2021
COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images.
Article in Biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 149 citations in OpenAlex.
- Integrating Local and Global Representation Learning for Pediatric Pneumonia Detection: A Hybrid CNN-Transformer Ensemble Framework.Diagnostics (Basel, Switzerland) · 2026Article
- Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.Clinical proteomics · 2026Review
- Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance.Journal of imaging informatics in medicine · 2025Article
- Diagnosing Respiratory Variability: Convolutional Neural Networks for Chest X-ray Classification Across Diverse Pulmonary Conditions.Journal of imaging informatics in medicine · 2025Article
- Evaluating the impact of view position in X-ray imaging for the classification of lung diseases.Physical and engineering sciences in medicine · 2025Article
- The Accuracy of ChatGPT-4o in Interpreting Chest and Abdominal X-Ray Images.Journal of personalized medicine · 2025Article
- Deep Learning for Automated Classification of Hip Hardware on Radiographs.Journal of imaging informatics in medicine · 2025Article
- An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation.Scientific reports · 2025Article
- Article
- X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs).Journal of imaging · 2024Article
- Removing Adversarial Noise in X-ray Images via Total Variation Minimization and Patch-Based Regularization for Robust Deep Learning-based Diagnosis.Journal of imaging informatics in medicine · 2024Article
- Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.Sensors (Basel, Switzerland) · 2024Article
- Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays.Scientific reports · 2024Article
- Deep learning for the harmonization of structural MRI scans: a survey.Biomedical engineering online · 2024Review
- On leveraging self-supervised learning for accurate HCV genotyping.Scientific reports · 2024Article
- Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images.Journal of imaging informatics in medicine · 2024Article
- Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers.Scientific reports · 2024Article
- Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images.Frontiers in public health · 2024Article
- Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis.PeerJ. Computer science · 2024Article
- Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study.BMC medical imaging · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.
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