ArticleScientific reports2021
GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Dental Caries diagnosis from bitewing images using convolutional neural networks.BMC oral health · 2024Article
- Multimodal graph attention network for COVID-19 outcome prediction.Scientific reports · 2023Article
- How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images.Scientific reports · 2023Article
- CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network.Foods (Basel, Switzerland) · 2023Article
- Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.New generation computing · 2023Article
- Prediction of the COVID disease using lung CT images by Deep Learning algorithm: DETS-optimized Resnet 101 classifier.Frontiers in medicine · 2023Article
- LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.Multimedia tools and applications · 2023Article
- Review of COVID-19 testing and diagnostic methods.Talanta · 2022Review
- COVID-WideNet-A capsule network for COVID-19 detection.Applied soft computing · 2022Article
- Improved COVID-19 detection with chest x-ray images using deep learning.Multimedia tools and applications · 2022Article
- Estimating the state of epidemics spreading with graph neural networks.Nonlinear dynamics · 2022Article
- A Secure and reliable RFID authentication protocol using digital schnorr cryptosystem for IoT-enabled healthcare in COVID-19 scenario.Sustainable cities and society · 2021Article
- Analytical and qualitative investigation of COVID-19 mathematical model under fractional differential operator.Mathematical methods in the applied sciences · 2021Article
- Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio.Computer communications · 2021Article
- Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection.Computers in biology and medicine · 2021Article
- Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
- Retraction · 2021-11-30Error in Analyses · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .
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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.