ArticleAdvances in engineering software (Barking, London, England : 1992)2023
A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.
Article in Advances in engineering software (Barking, London, England : 1992), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 104 citations in OpenAlex.
- Integrating CT image reconstruction, segmentation, and large language models for enhanced diagnostic insight.Medical & biological engineering & computing · 2026Article
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- A Flexible Multi-Channel Deep Network Leveraging Texture and Spatial Features for Diagnosing New COVID-19 Variants in Lung CT Scans.Tomography (Ann Arbor, Mich.) · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Enhancing COVID-19 classification of X-ray images with hybrid deep transfer learning models.Frontiers in artificial intelligence · 2025Article
- Modeling and control of COVID-19 disease using deep reinforcement learning method.Medical & biological engineering & computing · 2024Article
- Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images.Scientific reports · 2024Article
- Article
- Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection.Heliyon · 2024Article
- COVID-19 Detection and Diagnosis Model on CT Scans Based on AI Techniques.Bioengineering (Basel, Switzerland) · 2024Article
- Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges.Bioengineering (Basel, Switzerland) · 2023Article
- A novel investigations on medical and non-medical mask performance with influence of marine waste microplastics (polypropylene).Marine pollution bulletin · 2023Article
- A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.Soft computing · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.
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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.