Metaheuristics based COVID-19 detection using medical images: A review

Riaz, Mamoona; Bashir, Maryam; Younas, Irfan

doi:10.1016/j.compbiomed.2022.105344

Cited by 20 publications

(6 citation statements)

References 63 publications

(145 reference statements)

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“…The figure shows that both the 16-point Bayesian optimal designs require one or more observations at the extreme point T = 7 but no observation at the start of the study. They tend to be more concentrated in the interval [2,6] with that of estimating µ 3 more spread out. Because the CSO-MA generated designs optimize the design criterion among all 16-point designs, we can calculate efficiencies of the candidate designs.…”

Section: Bayesian Optimal Exact Designs For Nonlinear Mixed Models Ap...mentioning

confidence: 97%

“…For example, it is used widely in artificial intelligence [ 1 ] and during the pandemic, meta-heuristics was implemented in different ways to detect, track and predict COVID-19 infection rates. For example, [ 2 ] used meta-heuristics to develop cheap and fast diagnostic tools for detecting people infected with COVID19 to reduce the pressure on healthcare systems. Another example is [ 3 ], who created a discrete gaining-sharing knowledge-based meta-heuristic algorithm and derived an optimal disinfection scheduling strategies for cleaning public places during the pandemic.…”

Section: Introductionmentioning

confidence: 99%

“…[ 4 ] reviewed numerous applications of meta-heuristics to study COVID19. Meta-heuristics has also made significant contributions in using medical images to classify various diseases and predict disease progression in lung cancer, cardiovascular diseases and renal failure [ 5 , 6 ]. [ 7 ] discussed concrete applications of meta-heuristics to tackle healthcare and drug discovery issues.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Designs for Nonlinear Mixed-effects Models Using Competitive Swarm Optimizer with Mutated Agents

Cui,

Zhang,

Wong

2023

Preprint

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Nature-inspired meta-heuristic algorithms are increasingly used in many disciplines to tackle challenging optimization problems. Our focus is to apply a newly proposed nature-inspired meta-heuristics algorithm called CSO-MA to solve challenging design problems in biosciences and demonstrate its flexibility to find various types of optimal approximate or exact designs for nonlinear mixed models with one or several interacting factors and with or without random effects. We show that CSO-MA is efficient and can frequently outperform other algorithms either in terms of speed or accuracy. The algorithm, like other meta-heuristic algorithms, is free of technical assumptions and flexible in that it can incorporate cost structure or multiple user-specified constraints, such as, a fixed number of measurements per subject in a longitudinal study. When possible, we confirm some of the CSO-MA generated designs are optimal with theory by developing theory-based innovative plots. Our applications include searching optimal designs to estimate (i) parameters in mixed nonlinear models with correlated random effects, (ii) a function of parameters for a count model in a dose combination study, and (iii) parameters in a HIV dynamic model. In each case, we show the advantages of using a meta-heuristic approach to solve the optimization problem, and the added benefits of the generated designs.

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Section: Bayesian Optimal Exact Designs For Nonlinear Mixed Models Ap...mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Designs for Nonlinear Mixed-effects Models Using Competitive Swarm Optimizer with Mutated Agents

Cui,

Zhang,

Wong

2023

Preprint

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“…CT scan has demonstrated high sensitivity in identifying COVID-19 at the initial assessment of patients. In severe circumstances, it may efficiently correct RT-PCR false negatives [ 1 , 2 , 3 , 4 ]. Nevertheless, this is a complex and time-consuming process, because a professional must interpret the X-rays and CT imaging to establish if an individual is COVID-19 positive.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

Xue

Chinnaperumal²,

Abdulsahib

et al. 2023

Bioengineering

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Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses.

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“…However, a deep neural network’s architecture influences the performance of DL methodology [ 12 ]. Therefore, over the past 2 years of the pandemic, the use of meta-heuristics in optimizing the architecture and parameters of the deep neural network has gained significant popularity [ 13 ]. Furthermore, due to their simplicity, flexibility, and problem independence, researchers used meta-heuristics to solve various non-linear optimization problems [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction

2022

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The increase in coronavirus disease 2019 (COVID-19) infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed pressure on healthcare services worldwide. Therefore, it is crucial to identify critical factors for the assessment of the severity of COVID-19 infection and the optimization of an individual treatment strategy. In this regard, the present study leverages a dataset of blood samples from 485 COVID-19 individuals in the region of Wuhan, China to identify essential blood biomarkers that predict the mortality of COVID-19 individuals. For this purpose, a hybrid of filter, statistical, and heuristic-based feature selection approach was used to select the best subset of informative features. As a result, minimum redundancy maximum relevance (mRMR), a two-tailed unpaired t-test, and whale optimization algorithm (WOA) were eventually selected as the three most informative blood biomarkers: International normalized ratio (INR), platelet large cell ratio (P-LCR), and D-dimer. In addition, various machine learning (ML) algorithms (random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), naïve Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN)) were trained. The performance of the trained models was compared to determine the model that assist in predicting the mortality of COVID-19 individuals with higher accuracy, F1 score, and area under the curve (AUC) values. In this paper, the best performing RF-based model built using the three most informative blood parameters predicts the mortality of COVID-19 individuals with an accuracy of 0.96 ± 0.062, F1 score of 0.96 ± 0.099, and AUC value of 0.98 ± 0.024, respectively on the independent test data. Furthermore, the performance of our proposed RF-based model in terms of accuracy, F1 score, and AUC was significantly better than the known blood biomarkers-based ML models built using the Pre_Surv_COVID_19 data. Therefore, the present study provides a novel hybrid approach to screen the most informative blood biomarkers to develop an RF-based model, which accurately and reliably predicts in-hospital mortality of confirmed COVID-19 individuals, during surge periods. An application based on our proposed model was implemented and deployed at Heroku.

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Metaheuristics based COVID-19 detection using medical images: A review

Cited by 20 publications

References 63 publications

Optimal Designs for Nonlinear Mixed-effects Models Using Competitive Swarm Optimizer with Mutated Agents

Optimal Designs for Nonlinear Mixed-effects Models Using Competitive Swarm Optimizer with Mutated Agents

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction

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