ArticleDiagnostics (Basel, Switzerland)2021
COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals.Brain informatics · 2024Article
- A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI.PloS one · 2024Article
- Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems.Scientific reports · 2023Article
- RETRACTED ARTICLE: Covid-19 classification using sigmoid based hyper-parameter modified DNN for CT scans and chest X-rays.Multimedia tools and applications · 2023Article
- A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images.Journal of ambient intelligence and humanized computing · 2023Article
- Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods.Expert systems · 2022Article
- Metaheuristics based COVID-19 detection using medical images: A review.Computers in biology and medicine · 2022Review
- Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays.Neural computing & applications · 2022Article
- ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images.Multimedia tools and applications · 2022Article
- Speech as a Biomarker for COVID-19 Detection Using Machine Learning.Computational intelligence and neuroscience · 2022Article
- MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features.Scientific reports · 2021Article
- Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection.Computers in biology and medicine · 2021Article
- GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest.Scientific reports · 2021Article
- SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19.Biocybernetics and biomedical engineeringArticle
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5 authors.
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Abstract
The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.
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