ArticleExpert systems with applications2021
CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning.
Article in Expert systems with applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI.Bioengineering (Basel, Switzerland) · 2024Article
- An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis.Scientific reports · 2024Article
- Article
- Enhanced skin cancer diagnosis: a deep feature extraction-based framework for the multi-classification of skin cancer utilizing dermoscopy images.Frontiers in medicine · 2024Article
- Hierarchical Harris hawks optimizer for feature selection.Journal of advanced research · 2023Article
- A prospective approach to detect advanced persistent threats: Utilizing hybrid optimization technique.Heliyon · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Article
- Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.Expert systems with applications · 2023Article
- Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection.Journal of bionic engineering · 2023Article
- RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images.Expert systems with applications · 2022Article
- Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods.Expert systems · 2022Article
- A generic optimization and learning framework for Parkinson disease via speech and handwritten records.Journal of ambient intelligence and humanized computing · 2022Article
- Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images.BMC medical informatics and decision making · 2022Article
- Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System.Journal of personalized medicine · 2022Article
- Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model.Computational intelligence and neuroscience · 2022Article
- Ensemble deep learning for brain tumor detection.Frontiers in computational neuroscience · 2022Article
- An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer's-Based IoT System.Computational intelligence and neuroscience · 2022Article
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Starting from Wuhan in China at the end of 2019, coronavirus disease (COVID-19) has propagated fast all over the world, affecting the lives of billions of people and increasing the mortality rate worldwide in few months. The golden treatment against the invasive spread of COVID-19 is done by identifying and isolating the infected patients, and as a result, fast diagnosis of COVID-19 is a critical issue. The common laboratory test for confirming the infection of COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, these tests suffer from some problems in time, accuracy, and availability. Chest images have proven to be a powerful tool in the early detection of COVID-19. In the current study, a hybrid learning and optimization approach named CovH2SD is proposed for the COVID-19 detection from the Chest Computed Tomography (CT) images. CovH2SD uses deep learning and pre-trained models to extract the features from the CT images and learn from them. It uses Harris Hawks Optimization (HHO) algorithm to optimize the hyperparameters. Transfer learning is applied using nine pre-trained convolutional neural networks (i.e. ResNet50, ResNet101, VGG16, VGG19, Xception, MobileNetV1, MobileNetV2, DenseNet121, and DenseNet169). Fast Classification Stage (FCS) and Compact Stacking Stage (CSS) are suggested to stack the best models into a single one. Nine experiments are applied and results are reported based on the Loss, Accuracy, Precision, Recall, F1-Score, and Area Under Curve (AUC) performance metrics. The comparison between combinations is applied using the Weighted Sum Method (WSM). Six experiments report a WSM value above 96.5%. The top WSM and accuracy reported values are 99.31% and 99.33% respectively which are higher than the eleven compared state-of-the-art studies.
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