ArticlePattern recognition2022
SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network.
Article in Pattern recognition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- The role of artificial intelligence in managing COVID-19 and long COVID: a narrative review.Osong public health and research perspectives · 2026Article
- Adaptable graph neural networks design to support generalizability for clinical event prediction.Journal of biomedical informatics · 2025Article
- Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images.BMC medical imaging · 2024Article
- Innovative applications of artificial intelligence during the COVID-19 pandemic.Infectious medicine · 2024Review
- COVision: convolutional neural network for the differentiation of COVID-19 from common pulmonary conditions using CT scans.BMC pulmonary medicine · 2023Article
- COVID-19 infection inference with graph neural networks.Scientific reports · 2023Article
- Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images.Journal of King Saud University. Computer and information sciences · 2023Article
- Review on chest pathogies detection systems using deep learning techniques.Artificial intelligence review · 2023Article
- A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.An international journal on information fusion · 2023Article
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data.Frontiers in artificial intelligence · 2023Article
- Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture.SN computer science · 2023Review
- CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.Frontiers in public health · 2023Article
- Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.Computational intelligence and neuroscience · 2023Article
- COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.Artificial intelligence in medicine · 2022Article
- Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images.Bioengineering (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
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Abstract
COVID-19 has emerged as one of the deadliest pandemics that has ever crept on humanity. Screening tests are currently the most reliable and accurate steps in detecting severe acute respiratory syndrome coronavirus in a patient, and the most used is RT-PCR testing. Various researchers and early studies implied that visual indicators (abnormalities) in a patient's Chest X-Ray (CXR) or computed tomography (CT) imaging were a valuable characteristic of a COVID-19 patient that can be leveraged to find out virus in a vast population. Motivated by various contributions to open-source community to tackle COVID-19 pandemic, we introduce SARS-Net, a CADx system combining Graph Convolutional Networks and Convolutional Neural Networks for detecting abnormalities in a patient's CXR images for presence of COVID-19 infection in a patient. In this paper, we introduce and evaluate the performance of a custom-made deep learning architecture SARS-Net, to classify and detect the Chest X-ray images for COVID-19 diagnosis. Quantitative analysis shows that the proposed model achieves more accuracy than previously mentioned state-of-the-art methods. It was found that our proposed model achieved an accuracy of 97.60% and a sensitivity of 92.90% on the validation set.
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