ArticleIET image processing2021
COVID-19 disease severity assessment using CNN model.
Article in IET image processing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence.Journal of imaging · 2025Article
- Hyperspectral Imaging Techniques for Lyophilization: Advances in Data-Driven Modeling Strategies and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation.Diagnostics (Basel, Switzerland) · 2025Article
- A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.Scientific reports · 2025Article
- Lung pneumonia severity scoring in chest X-ray images using transformers.Medical & biological engineering & computing · 2024Article
- Discovering associations between radiological features and COVID-19 patients' deterioration.Health science reports · 2023Article
- Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning.Biomedical signal processing and control · 2023Article
- An innovative ensemble model based on deep learning for predicting COVID-19 infection.Scientific reports · 2023Article
- Biases associated with database structure for COVID-19 detection in X-ray images.Scientific reports · 2023Article
- COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm.Cluster computing · 2023Article
- Article
- Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities.Computational intelligence and neuroscience · 2023Review
- DeepCOVNet Model for COVID-19 Detection Using Chest X-Ray Images.Wireless personal communications · 2023Article
- COVID-19 detection and classification for machine learning methods using human genomic data.Measurement. Sensors · 2022Article
- Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.Quantitative imaging in medicine and surgery · 2022Article
- Machine Learning Approach for Autonomous Detection and Classification of COVID-19 Virus.Computers & electrical engineering : an international journal · 2022Article
- Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.Computer methods and programs in biomedicine · 2022Article
- CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.Applied soft computing · 2022Article
- LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images.Journal of personalized medicine · 2022Article
- COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model.Physical and engineering sciences in medicine · 2022Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the highly infectious nature of the novel coronavirus (COVID-19) disease, excessive number of patients waits in the line for chest X-ray examination, which overloads the clinicians and radiologists and negatively affects the patient's treatment, prognosis and control of the pandemic. Now that the clinical facilities such as the intensive care units and the mechanical ventilators are very limited in the face of this highly contagious disease, it becomes quite important to classify the patients according to their severity levels. This paper presents a novel implementation of convolutional neural network (CNN) approach for COVID-19 disease severity classification (assessment). An automated CNN model is designed and proposed to divide COVID-19 patients into four severity classes as mild, moderate, severe, and critical with an average accuracy of 95.52% using chest X-ray images as input. Experimental results on a sufficiently large number of chest X-ray images demonstrate the effectiveness of CNN model produced with the proposed framework. To the best of the author's knowledge, this is the first COVID-19 disease severity assessment study with four stages (mild vs. moderate vs. severe vs. critical) using a sufficiently large number of X-ray images dataset and CNN whose almost all hyper-parameters are automatically tuned by the grid search optimiser.
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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.