ArticleEntropy (Basel, Switzerland)2021
Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Combination Ensemble and Explainable Deep Learning Framework for High-Accuracy Classification of Wild Edible Macrofungi.Biology · 2025Article
- Levy flight-assisted hybrid Sine-Cosine Aquila optimizer for solving chemical equilibrium problems through the Gibbs free energy minimization technique.Scientific reports · 2025Article
- An online 11 kv distribution system insulator defect detection approach with modified YOLOv11 and mobileNetV3.Scientific reports · 2025Article
- Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.Scientific reports · 2025Article
- Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients.Scientific reports · 2025Article
- Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN.Scientific reports · 2024Article
- A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.Nature communications · 2024Article
- A Lightweight Model for Real-Time Detection of Vehicle Black Smoke.Sensors (Basel, Switzerland) · 2023Article
- Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs.Biomimetics (Basel, Switzerland) · 2023Article
- Automatic Detection Method for Black Smoke Vehicles Considering Motion Shadows.Sensors (Basel, Switzerland) · 2023Article
- Accurate Nonlinearity and Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Data Generation.Sensors (Basel, Switzerland) · 2023Article
- COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images.BMC medical imaging · 2023Article
- Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images.Multimedia tools and applications · 2023Article
- Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm.Sensors (Basel, Switzerland) · 2023Article
- Utilizing deep learning for dermal matrix quality assessment on in vivo line-field confocal optical coherence tomography images.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2023Article
- Effectiveness evaluation of different feature extraction methods for classification of covid-19 from computed tomography images: A high accuracy classification study.Biomedical signal processing and control · 2022Article
- COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review.Evolving systems · 2022Review
- Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm.Computational intelligence and neuroscience · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.
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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.