COVID-19 detection from CT scans using a two-stage framework

Basu, Arpan; Sheikh, Khalid Hassan; Cuevas, Erik; Sarkar, Ram

doi:10.1016/j.eswa.2021.116377

Cited by 58 publications

(25 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…COVID-19 detection using imaging modalities involves three crucial steps: 1) Data preparation 2) Acquisition of images 3) Diagnosis of the disease. These images can be easily analyzed using AI and image processing-based approaches (Basu et al 2022). Mathematical models have been implemented to predict the diagnosis using various demographic, laboratory, epidemiological and clinical markers .…”

Section: Introductionmentioning

confidence: 99%

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna

Chadaga

Sampathila

et al. 2022

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.

show abstract

Section: Introductionmentioning

confidence: 99%

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna

Chadaga

Sampathila

et al. 2022

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

show abstract

“…They applied the efficacy of this hybridization to diagnose pulmonary emphysema disease. Basu et al [ 29 ] presented a combination of harmony search and adaptive hill climbing approach to feature selection. The researchers used deep learning based on CNNs—convolutional neural networks to extract features.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

show abstract

“…The application of metaheuristic optimization algorithms to structural optimization is an active area of research. Among a large number of metaheuristic algorithms, the harmony search (HS) algorithm is one of the most widely used and established techniques, and applied to numerous areas such as structural design [ 23 ], water network design [ 24 ], flood model calibration [ 25 ], economic load dispatch [ 26 ], concrete mix proportion design [ 27 ], chaotic systems [ 28 ], timetabling [ 29 ], weapon target assignment [ 30 ], stock price prediction [ 31 ], mobile network security [ 32 ], COVID-19 detection from CT scans [ 33 ], and subway ventilation [ 34 ].…”

Section: Methods Of Optimization and Predictive Model Developmentmentioning

confidence: 99%

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

et al. 2022

View full text Add to dashboard Cite

This paper develops predictive models for optimal dimensions that minimize the construction cost associated with reinforced concrete retaining walls. Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were applied to obtain the predictive models. Predictive models were trained using a comprehensive dataset, which was generated using the Harmony Search (HS) algorithm. Each data sample in this database consists of a unique combination of the soil density, friction angle, ultimate bearing pressure, surcharge, the unit cost of concrete, and six different dimensions that describe an optimal retaining wall geometry. The influence of these design features on the optimal dimensioning and their interdependence are explained and visualized using the SHapley Additive exPlanations (SHAP) algorithm. The prediction accuracy of the used ensemble learning methods is evaluated with different metrics of accuracy such as the coefficient of determination, root mean square error, and mean absolute error. Comparing predicted and actual optimal dimensions on a test set showed that an R2 score of 0.99 could be achieved. In terms of computational speed, the LightGBM algorithm was found to be the fastest, with an average execution speed of 6.17 s for the training and testing of the model. On the other hand, the highest accuracy could be achieved by the CatBoost algorithm. The availability of open-source machine learning algorithms and high-quality datasets makes it possible for designers to supplement traditional design procedures with newly developed machine learning techniques. The novel methodology proposed in this paper aims at producing larger datasets, thereby increasing the applicability and accuracy of machine learning algorithms in relation to optimal dimensioning of structures.

show abstract

COVID-19 detection from CT scans using a two-stage framework

Cited by 58 publications

References 66 publications

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Multiclass feature selection with metaheuristic optimization algorithms: a review

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

Contact Info

Product

Resources

About