COVID-19 Diagnosis from CT Images with Convolutional Neural Network Optimized by Marine Predator Optimization Algorithm

Jia, Haitao; Zhao, Junwei; Arshaghi, Ali

doi:10.1155/2021/5122962

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…It gives them a local search characteristic. The optimization algorithms could be genetic algorithm (GA), particle swarm optimization (PSO), firefly algorithm, gray wolf algorithm, whale optimization algorithm, atom search optimization, sequential minimal optimization, and so on 41,42 …”

Section: Covid‐19 Diagnosis Methodsmentioning

confidence: 99%

“…The optimization algorithms could be genetic algorithm (GA), particle swarm optimization (PSO), firefly algorithm, gray wolf algorithm, whale optimization algorithm, atom search optimization, sequential minimal optimization, and so on. 41,42 Several researchers have developed DL-based approaches to detect COVID-19 cases using radiology images with promising results. However, the disease diagnosis becomes complex when there are cases of inadequate, ambiguous, noisy, and incorrect labels.…”

Section: Challenges With Manual Diagnosis Methodsmentioning

confidence: 99%

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COVID‐19: A systematic review of prediction and classification techniques

Varma,

Kalra,

Kirmani

2023

Int J Imaging Syst Tech

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COVID‐19 has affected more than 760 million people all over the world, as per the latest record of the WHO. The rapid proliferation of COVID‐19 patients not only created a health emergency but also led to an economic crisis. An early and accurate diagnosis of COVID‐19 can help in combating this deadly virus. In line with this, researchers have proposed several machine learning (ML) and deep learning (DL) techniques for detecting COVID‐19 since 2020. This article presents currently available manual diagnosis methods along with their limitations. It also provides an extensive survey of ML and DL techniques that can support medical professionals in the precise diagnosis of COVID‐19. ML methods, namely K‐nearest neighbor, support vector machine (SVM), artificial neural network, decision tree, naive bayes, and DL methods, viz. deep neural network, convolutional neural network (CNN), region‐based convolutional neural network, and long short‐term memories, are explored. It also provides details of the latest COVID‐19 open‐source datasets, consisting of x‐ray and computed tomography scan images. A comparative analysis of ML and DL techniques developed for COVID‐19 detection in terms of methodology, datasets, sample size, type of classification, performance, and limitations is also done. It has been found that SVM is the most frequently used ML technique, while CNN is the most commonly used DL technique for COVID‐19 detection. The challenges of an existing dataset have been identified, including size and quality of datasets, lack of labeled datasets, severity level, data imbalance, and privacy concerns. It is recommended that there is a need to establish a benchmark dataset that overcomes these challenges to enhance the effectiveness of ML and DL techniques. Further, hurdles in implementing ML and DL techniques in real‐time clinical settings have also been highlighted. In addition, the motivation noticed from the existing methods has been considered for extending the research with an optimized DL model, which attained improved performance using statistical and deep features. The optimized deep model performs better than 90% based on efficient features and proper classifier tuning.

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Section: Covid‐19 Diagnosis Methodsmentioning

confidence: 99%

Section: Challenges With Manual Diagnosis Methodsmentioning

confidence: 99%

COVID‐19: A systematic review of prediction and classification techniques

Varma,

Kalra,

Kirmani

2023

Int J Imaging Syst Tech

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“…Different intelligent optimization algorithms continue to drive advancements and transformations in the industrial sector and real-world applications. For instance, scheduling problems [11][12][13] , industrial manufacturing 14,15 , aviation 16,17 , facial recognition [18][19][20] , and medical imaging 21,22 , among others, have all seen the influence and application of various intelligent optimization algorithms.…”

mentioning

confidence: 99%

A whale optimization algorithm based on atom-like structure differential evolution for solving engineering design problems

Tang,

Wang

2024

Sci Rep

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The whale optimization algorithm has received much attention since its introduction due to its outstanding performance. However, like other algorithms, the whale optimization algorithm still suffers from some classical problems. To address the issues of slow convergence, low optimization precision, and susceptibility to local convergence in the whale optimization algorithm (WOA). Defining the optimization behavior of whale individuals as quantum mechanical behavior, a whale optimization algorithm based on atom-like structure differential evolution (WOAAD) is proposed. Enhancing the spiral update mechanism by introducing a sine strategy guided by the electron orbital center. Improving the random-walk foraging mechanism by applying mutation operations to both the electron orbital center and random individuals. Performing crossover operations between the newly generated individuals from the improved mechanisms and random dimensions, followed by a selection process to retain superior individuals. This accelerates algorithm convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Finally, implementing a scouting bee strategy, where whale individuals progressively increase the number of optimization failures within a limited parameter L. When a threshold is reached, random initialization is carried out to enhance population diversity. Conducting simulation experiments to compare the improved algorithm with the whale optimization algorithm, other optimization algorithms, and other enhanced whale optimization algorithms. The experimental results indicate that the improved algorithm significantly accelerates convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Applying the improved algorithm to five engineering design problems, the experimental results demonstrate that the improved algorithm exhibits good applicability.

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Marine Predators Algorithm: A Review

Al-Betar

Awadallah

Makhadmeh

et al. 2023

Arch Computat Methods Eng

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Marine Predators Algorithm (MPA) is a recent nature-inspired optimizer stemmed from widespread foraging mechanisms based on Lévy and Brownian movements in ocean predators. Due to its superb features, such as derivative-free, parameter-less, easy-to-use, flexible, and simplicity, MPA is quickly evolved for a wide range of optimization problems in a short period. Therefore, its impressive characteristics inspire this review to analyze and discuss the primary MPA research studies established. In this review paper, the growth of the MPA is analyzed based on 102 research papers to show its powerful performance. The MPA inspirations and its theoretical concepts are also illustrated, focusing on its convergence behaviour. Thereafter, the MPA versions suggested improving the MPA behaviour on connecting the search space shape of real-world optimization problems are analyzed. A plethora and diverse optimization applications have been addressed, relying on MPA as the main solver, which is also described and organized. In addition, a critical discussion about the convergence behaviour and the main limitation of MPA is given. The review is end-up highlighting the main findings of this survey and suggests some possible MPA-related improvements and extensions that can be carried out in the future.

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COVID-19 Diagnosis from CT Images with Convolutional Neural Network Optimized by Marine Predator Optimization Algorithm

Cited by 3 publications

References 24 publications

COVID‐19: A systematic review of prediction and classification techniques

COVID‐19: A systematic review of prediction and classification techniques

A whale optimization algorithm based on atom-like structure differential evolution for solving engineering design problems

Marine Predators Algorithm: A Review

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