ArticleNeurocomputing2021
ResGNet-C: A graph convolutional neural network for detection of COVID-19.
Article in Neurocomputing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Federated autoencoder-based clinical decision framework with hybrid class balancing.Scientific reports · 2026Article
- Multimodal text guided network for chest CT pneumonia classification.Scientific reports · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images.iScience · 2024Article
- DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.Sensors (Basel, Switzerland) · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Multimodal graph attention network for COVID-19 outcome prediction.Scientific reports · 2023Article
- Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.Applied soft computing · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- The importance of graph databases and graph learning for clinical applications.Database : the journal of biological databases and curation · 2023Review
- Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network.Multimedia tools and applications · 2023Article
- Article
- Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision.Expert systems with applications · 2022Article
- Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review.Healthcare (Basel, Switzerland) · 2022Review
- ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification With Chest X-Rays.IEEE transactions on medical imaging · 2022Article
- COVID-19 prognosis using limited chest X-ray images.Applied soft computing · 2022Article
- Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images.Neurocomputing · 2022Article
- COVID-19 detection from CT scans using a two-stage framework.Expert systems with applications · 2022Article
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
The widely spreading COVID-19 has caused thousands of hundreds of mortalities over the world in the past few months. Early diagnosis of the virus is of great significance for both of infected patients and doctors providing treatments. Chest Computerized tomography (CT) screening is one of the most straightforward techniques to detect pneumonia which was caused by the virus and thus to make the diagnosis. To facilitate the process of diagnosing COVID-19, we therefore developed a graph convolutional neural network ResGNet-C under ResGNet framework to automatically classify lung CT images into normal and confirmed pneumonia caused by COVID-19. In ResGNet-C, two by-products named NNet-C, ResNet101-C that showed high performance on detection of COVID-19 are simultaneously generated as well. Our best model ResGNet-C achieved an averaged accuracy at 0.9662 with an averaged sensitivity at 0.9733 and an averaged specificity at 0.9591 using five cross-validations on the dataset, which is comprised of 296 CT images. To our best knowledge, this is the first attempt at integrating graph knowledge into the COVID-19 classification task. Graphs are constructed according to the Euclidean distance between features extracted by our proposed ResNet101-C and then are encoded with the features to give the prediction results of CT images. Besides the high-performance system, which surpassed all state-of-the-art methods, our proposed graph construction method is simple, transferrable yet quite helpful for improving the performance of classifiers, as can be justified by the experimental results.
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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.