ArticleIEEE access : practical innovations, open solutions2020
Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
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- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.Diagnostics (Basel, Switzerland) · 2023Article
- Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19.Computer methods and programs in biomedicine · 2023Article
- SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition.Computer systems science and engineering · 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
- Feature selection in wind speed forecasting systems based on meta-heuristic optimization.PloS one · 2023Article
- LCCNN: a Lightweight Customized CNN-Based Distance Education App for COVID-19 Recognition.Mobile networks and applications : MONET · 2023Article
- E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.Engineering applications of artificial intelligence · 2022Article
- The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient.Sensors (Basel, Switzerland) · 2022Article
- A novel deep fusion strategy for COVID-19 prediction using multimodality approach.Computers & electrical engineering : an international journal · 2022Article
- IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.Sensors (Basel, Switzerland) · 2022Article
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5 authors.
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Abstract
Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This article proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers' predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.
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