Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images DOI Creative Commons
Amal Alshardan,

Nuha Alruwais,

Hamed Alqahtani

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 18, 2024

Recognition and segmentation of brain tumours (BT) using MR images are valuable tedious processes in the healthcare industry. Earlier diagnosis localization BT provide timely options to select effective treatment plans for doctors can save lives. from Magnetic Resonance Images (MRI) is considered a big challenge owing difficulty tissues, segmenting them healthier tissue challenging when manual done through radiologists. Among recent proposals method, method based on machine learning (ML) image processing could be better. Thus, DL-based extensively applied, convolutional network has better effects. The deep model problem large loss information number parameters encoding decoding processes. With this motivation, article presents new Deep Transfer Learning with Semantic Segmentation Medical Image Analysis (DTLSS-MIA) technique MRI images. DTLSS-MIA aims segment affected area At first, presented utilizes Median filtering (MF) approach optimize quality remove noise. For semantic follows DeepLabv3 + backbone EfficientNet determining region. Moreover, CapsNet architecture employed feature extraction process. Lastly, crayfish optimization (CFO) diffusion variational autoencoder (D-VAE) used as classification mechanism, CFO effectively tunes D-VAE hyperparameter. simulation analysis validated benchmark dataset. performance validation exhibited superior accuracy value 99.53% over other methods.

Язык: Английский

Enhanced crayfish optimization algorithm with differential evolution’s mutation and crossover strategies for global optimization and engineering applications DOI Creative Commons
Biswajit Maiti, Saptadeep Biswas, Absalom E. Ezugwu

и другие.

Artificial Intelligence Review, Год журнала: 2025, Номер 58(3)

Опубликована: Янв. 6, 2025

Abstract Optimization algorithms play a crucial role in solving complex challenges across various fields, including engineering, finance, and data science. This study introduces novel hybrid optimization algorithm, the Hybrid Crayfish Algorithm with Differential Evolution (HCOADE), which addresses limitations of premature convergence inadequate exploitation traditional (COA). By integrating COA (DE) strategies, HCOADE leverages DE’s mutation crossover mechanisms to enhance global performance. The COA, inspired by foraging social behaviors crayfish, provides flexible framework for exploring solution space, while robust strategies effectively exploit this space. To evaluate HCOADE’s performance, extensive experiments are conducted using 34 benchmark functions from CEC 2014 2017, as well six engineering design problems. results compared ten leading algorithms, classical Particle Swarm (PSO), Grey Wolf Optimizer (GWO), Whale (WOA), Moth-flame (MFO), Salp (SSA), Reptile Search (RSA), Sine Cosine (SCA), Constriction Coefficient-Based Gravitational (CPSOGSA), Biogeography-based (BBO). average rankings Wilcoxon Rank Sum Test provide comprehensive comparison clearly demonstrating its superiority. Furthermore, performance is assessed on 2020 2022 test suites, further confirming effectiveness. A comparative analysis against notable winners competitions, LSHADEcnEpSin, LSHADESPACMA, CMA-ES, CEC-2017 suite, revealed superior HCOADE. underscores advantages DE offers valuable insights addressing

Язык: Английский

Процитировано

4
Boosting crayfish algorithm based on halton adaptive quadratic interpolation and piecewise neighborhood for complex optimization problems DOI
Mahmoud Abdel-Salam, Laith Abualigah, Ahmed Ibrahim Alzahrani

и другие.

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 432, С. 117429 - 117429

Опубликована: Окт. 9, 2024

Язык: Английский

Процитировано

4
An Improved Crayfish Optimization Algorithm: Enhanced Search Efficiency and Application to UAV Path Planning DOI Open Access
Qinyuan Huang,

Yuqi Sun,

CongBao Kang

и другие.

Symmetry, Год журнала: 2025, Номер 17(3), С. 356 - 356

Опубликована: Фев. 26, 2025

The resolution of the unmanned aerial vehicle (UAV) path-planning problem frequently leverages optimization algorithms as a foundational approach. Among these, recently proposed crayfish algorithm (COA) has garnered significant attention promising and noteworthy alternative. Nevertheless, COA’s search efficiency tends to diminish in later stages process, making it prone premature convergence into local optima. To address this limitation, an improved COA (ICOA) is proposed. enhance quality initial individuals ensure greater population diversity, utilizes chaotic mapping conjunction with stochastic inverse learning strategy generate population. This modification aims broaden exploration scope higher-quality regions, enhancing algorithm’s resilience against optima entrapment significantly boosting its effectiveness. Additionally, nonlinear control parameter incorporated adaptivity. Simultaneously, Cauchy variation applied population’s optimal individuals, strengthening ability overcome stagnation. ICOA’s performance evaluated by employing IEEE CEC2017 benchmark function for testing purposes. Comparison results reveal that ICOA outperforms other terms efficacy, especially when complex spatial configurations real-world problem-solving scenarios. ultimately employed UAV path planning, tested across range terrain obstacle models. findings confirm excels searching paths achieve safe avoidance lower trajectory costs. Its accuracy notably superior comparative algorithms, underscoring robustness efficiency. ensures balanced exploitation space, which are particularly crucial optimizing planning environments symmetrical asymmetrical constraints.

Язык: Английский

Процитировано

0
A Reinforcement Learning-Based Bi-Population Nutcracker Optimizer for Global Optimization DOI Creative Commons
Yu Li, Yan Zhang

Biomimetics, Год журнала: 2024, Номер 9(10), С. 596 - 596

Опубликована: Окт. 1, 2024

The nutcracker optimizer algorithm (NOA) is a metaheuristic method proposed in recent years. This simulates the behavior of nutcrackers searching and storing food nature to solve optimization problem. However, traditional NOA struggles balance global exploration local exploitation effectively, making it prone getting trapped optima when solving complex problems. To address these shortcomings, this study proposes reinforcement learning-based bi-population called RLNOA. In RLNOA, mechanism introduced better capabilities. At beginning each iteration, raw population divided into an sub-population based on fitness value individual. composed individuals with poor values. An improved foraging strategy random opposition-based learning designed as update for enhance diversity. Meanwhile, Q-learning serves adaptive selector strategies, enabling optimal adjustment sub-population’s across various performance RLNOA evaluated using CEC-2014, CEC-2017, CEC-2020 benchmark function sets, compared against nine state-of-the-art algorithms. Experimental results demonstrate superior algorithm.

Язык: Английский

Процитировано

0
Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images DOI Creative Commons
Amal Alshardan,

Nuha Alruwais,

Hamed Alqahtani

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 18, 2024

Recognition and segmentation of brain tumours (BT) using MR images are valuable tedious processes in the healthcare industry. Earlier diagnosis localization BT provide timely options to select effective treatment plans for doctors can save lives. from Magnetic Resonance Images (MRI) is considered a big challenge owing difficulty tissues, segmenting them healthier tissue challenging when manual done through radiologists. Among recent proposals method, method based on machine learning (ML) image processing could be better. Thus, DL-based extensively applied, convolutional network has better effects. The deep model problem large loss information number parameters encoding decoding processes. With this motivation, article presents new Deep Transfer Learning with Semantic Segmentation Medical Image Analysis (DTLSS-MIA) technique MRI images. DTLSS-MIA aims segment affected area At first, presented utilizes Median filtering (MF) approach optimize quality remove noise. For semantic follows DeepLabv3 + backbone EfficientNet determining region. Moreover, CapsNet architecture employed feature extraction process. Lastly, crayfish optimization (CFO) diffusion variational autoencoder (D-VAE) used as classification mechanism, CFO effectively tunes D-VAE hyperparameter. simulation analysis validated benchmark dataset. performance validation exhibited superior accuracy value 99.53% over other methods.

Язык: Английский

Процитировано

0