ArticleScientific reports2020
COVID-19 image classification using deep features and fractional-order marine predators algorithm.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Knacks of marine predator heuristics for distributed energy source-based power systems harmonics estimation.Heliyon · 2024Article
- CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes.Diagnostics (Basel, Switzerland) · 2024Article
- An improved mountain gazelle optimizer based on chaotic map and spiral disturbance for medical feature selection.PloS one · 2024Article
- Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture.Viruses · 2023Article
- Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection.Heliyon · 2023Article
- Cross-platform privacy-preserving CT image COVID-19 diagnosis based on source-free domain adaptation.Knowledge-based systems · 2023Article
- Robust Classification and Detection of Big Medical Data Using Advanced ParallelLife (Basel, Switzerland) · 2023Article
- Changes in physicochemical, heavy metals and air quality linked to spot Aplocheilus panchax along Mahanadi industrial belt of India under COVID-19-induced lockdowns.Environmental geochemistry and health · 2023Article
- A Multi-strategy Improved Outpost and Differential Evolution Mutation Marine Predators Algorithm for Global Optimization.Arabian journal for science and engineering · 2023Article
- UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.An international journal on information fusion · 2023Article
- An Inclusive Survey on Marine Predators Algorithm: Variants and Applications.Archives of computational methods in engineering : state of the art reviews · 2023Review
- COVID-19 lateral flow test image classification using deep CNN and StyleGAN2.Frontiers in artificial intelligence · 2023Article
- Article
- Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection.Computational intelligence and neuroscience · 2023Article
- Marine Predators Algorithm: A Review.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Enhanced feature selection technique using slime mould algorithm: a case study on chemical data.Neural computing & applications · 2023Article
- A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.Multimedia tools and applications · 2023Article
12 more citing papers are in PubMed but not listed here.
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images.
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