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Qu P, Yuan Q, Du F, Gao Q. An improved manta ray foraging optimization algorithm. Sci Rep 2024; 14:10301. [PMID: 38705906 PMCID: PMC11070432 DOI: 10.1038/s41598-024-59960-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms.
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Affiliation(s)
- Pengju Qu
- Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China
- Engineering Training Center, Guizhou Institute of Technology, Guiyang, China
| | - Qingni Yuan
- Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China.
| | - Feilong Du
- Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China
| | - Qingyang Gao
- Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China
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2
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Houssein EH, Hammad A, Emam MM, Ali AA. An enhanced Coati Optimization Algorithm for global optimization and feature selection in EEG emotion recognition. Comput Biol Med 2024; 173:108329. [PMID: 38513391 DOI: 10.1016/j.compbiomed.2024.108329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 03/23/2024]
Abstract
Emotion recognition based on Electroencephalography (EEG) signals has garnered significant attention across diverse domains including healthcare, education, information sharing, and gaming, among others. Despite its potential, the absence of a standardized feature set poses a challenge in efficiently classifying various emotions. Addressing the issue of high dimensionality, this paper introduces an advanced variant of the Coati Optimization Algorithm (COA), called eCOA for global optimization and selecting the best subset of EEG features for emotion recognition. Specifically, COA suffers from local optima and imbalanced exploitation abilities as other metaheuristic methods. The proposed eCOA incorporates the COA and RUNge Kutta Optimizer (RUN) algorithms. The Scale Factor (SF) and Enhanced Solution Quality (ESQ) mechanism from RUN are applied to resolve the raised shortcomings of COA. The proposed eCOA algorithm has been extensively evaluated using the CEC'22 test suite and two EEG emotion recognition datasets, DEAP and DREAMER. Furthermore, the eCOA is applied for binary and multi-class classification of emotions in the dimensions of valence, arousal, and dominance using a multi-layer perceptron neural network (MLPNN). The experimental results revealed that the eCOA algorithm has more powerful search capabilities than the original COA and seven well-known counterpart methods related to statistical, convergence, and diversity measures. Furthermore, eCOA can efficiently support feature selection to find the best EEG features to maximize performance on four quadratic emotion classification problems compared to the methods of its counterparts. The suggested method obtains a classification accuracy of 85.17% and 95.21% in the binary classification of low and high arousal emotions in two public datasets: DEAP and DREAMER, respectively, which are 5.58% and 8.98% superior to existing approaches working on the same datasets for different subjects, respectively.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Asmaa Hammad
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Abdelmgeid A Ali
- Faculty of Computers and Information, Minia University, Minia, Egypt.
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3
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Zhu D, Shen J, Zheng Y, Li R, Zhou C, Cheng S, Yao Y. Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation. Comput Biol Med 2024; 176:108498. [PMID: 38744011 DOI: 10.1016/j.compbiomed.2024.108498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/09/2024] [Accepted: 04/21/2024] [Indexed: 05/16/2024]
Abstract
With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm.
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Affiliation(s)
- Donglin Zhu
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.
| | - Jiaying Shen
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.
| | - Yangyang Zheng
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.
| | - Rui Li
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.
| | - Changjun Zhou
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.
| | - Shi Cheng
- School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
| | - Yilin Yao
- College of Software Engineering, Jiangxi University of Science and Technology, Nanchang, 330013, China.
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4
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Youssef H, Kamel S, Hassan MH. Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization. Sci Rep 2023; 13:22163. [PMID: 38092942 PMCID: PMC10719324 DOI: 10.1038/s41598-023-49176-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023] Open
Abstract
This paper proposes a plan to manage energy consumption in residential areas using the demand response method, which allows electricity users to contribute to the reliability of the power system by controlling their usage. Due to the growing population, the residential sector consumes a significant amount of energy, and the objectives of this study are to lower electricity costs and the peak to average ratio, as well as reduce the amount of imported electricity from the grid. The study aims to maximize profit by properly utilizing renewable energy sources and addressing energy trading. The manta ray foraging optimization (MRFO) and long term memory MRFO (LMMRFO) algorithms are used to solve this problem. Firstly, the validation of the proposed LMMRFO technique is confirmed by seven benchmark functions and compared its results with the results of the well-known optimization algorithms including hunter prey optimization, gorilla troops optimizer, beluga whale optimization, and the original MRFO algorithm. Then, the performance of the LMMRFO is checked on the optimization of smart home energy management. In the suggested approach, a smart home decides whether to purchase or sell electricity from the commercial grid based on the cost, demand, and production of electricity from its own microgrid, which consists of a wind turbine and solar panels. Energy storage systems support the stable and dependable functioning of the power system since the solar panel and wind turbine only occasionally produce electricity. Through various case studies, the proposed plan is tested and found to be effective in reducing electricity costs and the peak to average ratio while maximizing profit. Furthermore, a comparative study is conducted to demonstrate the legality and effectiveness of LMMRFO and MRFO.
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Affiliation(s)
- Heba Youssef
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswân, 81542, Egypt
| | - Salah Kamel
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswân, 81542, Egypt.
| | - Mohamed H Hassan
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswân, 81542, Egypt
- Ministry of Electricity and Renewable Energy, Cairo, Egypt
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5
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Emam MM, Houssein EH, Tolba MA, Zaky MM, Hamouda Ali M. Application of modified artificial hummingbird algorithm in optimal power flow and generation capacity in power networks considering renewable energy sources. Sci Rep 2023; 13:21446. [PMID: 38052877 DOI: 10.1038/s41598-023-48479-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/27/2023] [Indexed: 12/07/2023] Open
Abstract
Today's electrical power system is a complicated network that is expanding rapidly. The power transmission lines are more heavily loaded than ever before, which causes a host of problems like increased power losses, unstable voltage, and line overloads. Real and reactive power can be optimized by placing energy resources at appropriate locations. Congested networks benefit from this to reduce losses and enhance voltage profiles. Hence, the optimal power flow problem (OPF) is crucial for power system planning. As a result, electricity system operators can meet electricity demands efficiently and ensure the reliability of the power systems. The classical OPF problem ignores network emissions when dealing with thermal generators with limited fuel. Renewable energy sources are becoming more popular due to their sustainability, abundance, and environmental benefits. This paper examines modified IEEE-30 bus and IEEE-118 bus systems as case studies. Integrating renewable energy sources into the grid can negatively affect its performance without adequate planning. In this study, control variables were optimized to minimize fuel cost, real power losses, emission cost, and voltage deviation. It also met operating constraints, with and without renewable energy. This solution can be further enhanced by the placement of distributed generators (DGs). A modified Artificial Hummingbird Algorithm (mAHA) is presented here as an innovative and improved optimizer. In mAHA, local escape operator (LEO) and opposition-based learning (OBL) are integrated into the basic Artificial Hummingbird Algorithm (AHA). An improved version of AHA, mAHA, seeks to improve search efficiency and overcome limitations. With the CEC'2020 test suite, the mAHA has been compared to several other meta-heuristics for addressing global optimization challenges. To test the algorithm's feasibility, standard and modified test systems were used to solve the OPF problem. To assess the effectiveness of mAHA, the results were compared to those of seven other global optimization algorithms. According to simulation results, the proposed algorithm minimized the cost function and provided convergent solutions.
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Affiliation(s)
- Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Mohamed A Tolba
- Reactors Department, Nuclear Research Center, Egyptian Atomic Energy Authority (EAEA), Cairo, 11787, Egypt
| | - Magdy M Zaky
- Engineering Department, Nuclear Research Center, ETRR-2, Atomic Energy Authority (EAEA), Cairo, 11787, Egypt
| | - Mohammed Hamouda Ali
- Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo, 11651, Egypt.
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6
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Houssein EH, Oliva D, Samee NA, Mahmoud NF, Emam MM. Liver Cancer Algorithm: A novel bio-inspired optimizer. Comput Biol Med 2023; 165:107389. [PMID: 37678138 DOI: 10.1016/j.compbiomed.2023.107389] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/04/2023] [Accepted: 08/25/2023] [Indexed: 09/09/2023]
Abstract
This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor's ability to replicate and spread to other organs inspires the algorithm. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm's efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC'2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge-Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Diego Oliva
- Depto. Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara, Jal, Mexico.
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Noha F Mahmoud
- Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt.
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7
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Kumar Sahoo S, Houssein EH, Premkumar M, Kumar Saha A, Emam MM. Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation. Expert Syst Appl 2023; 227:120367. [PMID: 37193000 PMCID: PMC10163947 DOI: 10.1016/j.eswa.2023.120367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/15/2023] [Accepted: 05/01/2023] [Indexed: 05/18/2023]
Abstract
The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.
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Affiliation(s)
- Saroj Kumar Sahoo
- Department of Mathematics, National Institute of Technology Agartala, Tripura 799046, India
| | - Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - M Premkumar
- Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India
| | - Apu Kumar Saha
- Department of Mathematics, National Institute of Technology Agartala, Tripura 799046, India
| | - Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt
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8
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Kanadath A, Jothi JAA, Urolagin S. Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques. SN Comput Sci 2023; 4:427. [PMID: 37304839 PMCID: PMC10245360 DOI: 10.1007/s42979-023-01915-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/16/2023] [Indexed: 06/13/2023]
Abstract
Histopathology image segmentation is a challenging task in medical image processing. This work aims to segment lesion regions from colonoscopy histopathology images. Initially, the images are preprocessed and then segmented using the multilevel image thresholding technique. Multilevel thresholding is considered an optimization problem. Particle swarm optimization (PSO) and its variants, darwinian particle swarm optimization (DPSO), and fractional order darwinian particle swarm optimization (FODPSO) are used to solve the optimization problem and they generate the threshold values. The threshold values obtained are used to segment the lesion regions from the images of the colonoscopy tissue data set. Segmented images containing the lesion regions are then postprocessed to remove unnecessary regions. Experimental results reveal that the FODPSO algorithm with Otsu's discriminant criterion as the objective function achieves the best accuracy, Dice and Jaccard values of 0.89, 0.68 and 0.52, respectively, for the colonoscopy data set. The FODPSO algorithm also outperforms other optimization methods such as artificial bee colony and the firefly algorithms in terms of the accuracy, Dice and Jaccard values.
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Affiliation(s)
- Anusree Kanadath
- Department of Computer Science, Birla Institute of Science and Technology Pilani, Dubai Campus, Dubai International Academic City, Dubai, 345055 United Arab Emirates
| | - J Angel Arul Jothi
- Department of Computer Science, Birla Institute of Science and Technology Pilani, Dubai Campus, Dubai International Academic City, Dubai, 345055 United Arab Emirates
| | - Siddhaling Urolagin
- Department of Computer Science, Birla Institute of Science and Technology Pilani, Dubai Campus, Dubai International Academic City, Dubai, 345055 United Arab Emirates
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9
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Houssein EH, Mohamed GM, Ibrahim IA, Wazery YM. An efficient multilevel image thresholding method based on improved heap-based optimizer. Sci Rep 2023; 13:9094. [PMID: 37277531 DOI: 10.1038/s41598-023-36066-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/29/2023] [Indexed: 06/07/2023] Open
Abstract
Image segmentation is the process of separating pixels of an image into multiple classes, enabling the analysis of objects in the image. Multilevel thresholding (MTH) is a method used to perform this task, and the problem is to obtain an optimal threshold that properly segments each image. Methods such as the Kapur entropy or the Otsu method, which can be used as objective functions to determine the optimal threshold, are efficient in determining the best threshold for bi-level thresholding; however, they are not effective for MTH due to their high computational cost. This paper integrates an efficient method for MTH image segmentation called the heap-based optimizer (HBO) with opposition-based learning termed improved heap-based optimizer (IHBO) to solve the problem of high computational cost for MTH and overcome the weaknesses of the original HBO. The IHBO was proposed to improve the convergence rate and local search efficiency of search agents of the basic HBO, the IHBO is applied to solve the problem of MTH using the Otsu and Kapur methods as objective functions. The performance of the IHBO-based method was evaluated on the CEC'2020 test suite and compared against seven well-known metaheuristic algorithms including the basic HBO, salp swarm algorithm, moth flame optimization, gray wolf optimization, sine cosine algorithm, harmony search optimization, and electromagnetism optimization. The experimental results revealed that the proposed IHBO algorithm outperformed the counterparts in terms of the fitness values as well as other performance indicators, such as the structural similarity index (SSIM), feature similarity index (FSIM), peak signal-to-noise ratio. Therefore, the IHBO algorithm was found to be superior to other segmentation methods for MTH image segmentation.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Gaber M Mohamed
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Ibrahim A Ibrahim
- Faculty of Computers and Information, Minia University, Minia, Egypt
| | - Yaser M Wazery
- Faculty of Computers and Information, Minia University, Minia, Egypt
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10
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Emam MM, Samee NA, Jamjoom MM, Houssein EH. Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm. Comput Biol Med 2023; 160:106966. [PMID: 37141655 DOI: 10.1016/j.compbiomed.2023.106966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/05/2023] [Accepted: 04/19/2023] [Indexed: 05/06/2023]
Abstract
One of the worst diseases is a brain tumor, which is defined by abnormal development of synapses in the brain. Early detection of brain tumors is essential for improving prognosis, and classifying tumors is a vital step in the disease's treatment. Different classification strategies using deep learning have been presented for the diagnosis of brain tumors. However, several challenges exist, such as the need for a competent specialist in classifying brain cancers by deep learning models and the problem of building the most precise deep learning model for categorizing brain tumors. We propose an evolved and highly efficient model based on deep learning and improved metaheuristic algorithms to address these challenges. Specifically, we develop an optimized residual learning architecture for classifying multiple brain tumors and propose an improved variant of the Hunger Games Search algorithm (I-HGS) based on combining two enhancing strategies: Local Escaping Operator (LEO) and Brownian motion. These two strategies balance solution diversity and convergence speed, boosting the optimization performance and staying away from the local optima. First, we have evaluated the I-HGS algorithm on the IEEE Congress on Evolutionary Computation held in 2020 (CEC'2020) test functions, demonstrating that I-HGS outperformed the basic HGS and other popular algorithms regarding statistical convergence, and various measures. The suggested model is then applied to the optimization of the hyperparameters of the Residual Network 50 (ResNet50) model (I-HGS-ResNet50) for brain cancer identification, proving its overall efficacy. We utilize several publicly available, gold-standard datasets of brain MRI images. The proposed I-HGS-ResNet50 model is compared with other existing studies as well as with other deep learning architectures, including Visual Geometry Group 16-layer (VGG16), MobileNet, and Densely Connected Convolutional Network 201 (DenseNet201). The experiments demonstrated that the proposed I-HGS-ResNet50 model surpasses the previous studies and other well-known deep learning models. I-HGS-ResNet50 acquired an accuracy of 99.89%, 99.72%, and 99.88% for the three datasets. These results efficiently prove the potential of the proposed I-HGS-ResNet50 model for accurate brain tumor classification.
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Affiliation(s)
- Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
| | - Mona M Jamjoom
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
| | - Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
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11
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Shi M, Chen C, Liu L, Kuang F, Zhao D, Chen X. A grade-based search adaptive random slime mould optimizer for lupus nephritis image segmentation. Comput Biol Med 2023; 160:106950. [PMID: 37120988 DOI: 10.1016/j.compbiomed.2023.106950] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/04/2023] [Accepted: 04/15/2023] [Indexed: 05/02/2023]
Abstract
The segmentation of medical images is a crucial and demanding step in medical image processing that offers a solid foundation for subsequent extraction and analysis of medical image data. Although multi-threshold image segmentation is the most used and specialized basic image segmentation technique, it is computationally demanding and often produces subpar segmentation results, hence restricting its application. To solve this issue, this work develops a multi-strategy-driven slime mould algorithm (RWGSMA) for multi-threshold image segmentation. Specifically, the random spare strategy, the double adaptive weigh strategy, and the grade-based search strategy are used to improve the performance of SMA, resulting in an enhanced SMA version. The random spare strategy is mainly used to accelerate the convergence rate of the algorithm. To prevent SMA from falling towards the local optimum, the double adaptive weights are also applied. The grade-based search approach has also been developed to boost convergence performance. This study evaluates the efficacy of RWGSMA from many viewpoints using 30 test suites from IEEE CEC2017 to effectively demonstrate the importance of these techniques in RWGSMA. In addition, numerous typical images were used to show RWGSMA's segmentation performance. Using the multi-threshold segmentation approach with 2D Kapur's entropy as the RWGSMA fitness function, the suggested algorithm was then used to segment instances of lupus nephritis. The experimental findings demonstrate that the suggested RWGSMA beats numerous similar rivals, suggesting that it has a great deal of promise for segmenting histopathological images.
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Affiliation(s)
- Manrong Shi
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.
| | - Chi Chen
- Wenzhou University of Technology, Wenzhou, 325035, China.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Fangjun Kuang
- School of Information engineering, Wenzhou Business College, Wenzhou, 325035, China.
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University, Changchun, Jilin, 130032, China.
| | - Xiaowei Chen
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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12
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Houssein EH, Mohamed GM, Abdel Samee N, Alkanhel R, Ibrahim IA, Wazery YM. An Improved Search and Rescue Algorithm for Global Optimization and Blood Cell Image Segmentation. Diagnostics (Basel) 2023; 13:diagnostics13081422. [PMID: 37189523 DOI: 10.3390/diagnostics13081422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/08/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Image segmentation has been one of the most active research areas in the last decade. The traditional multi-level thresholding techniques are effective for bi-level thresholding because of their resilience, simplicity, accuracy, and low convergence time, but these traditional techniques are not effective in determining the optimal multi-level thresholding for image segmentation. Therefore, an efficient version of the search and rescue optimization algorithm (SAR) based on opposition-based learning (OBL) is proposed in this paper to segment blood-cell images and solve problems of multi-level thresholding. The SAR algorithm is one of the most popular meta-heuristic algorithms (MHs) that mimics humans' exploration behavior during search and rescue operations. The SAR algorithm, which utilizes the OBL technique to enhance the algorithm's ability to jump out of the local optimum and enhance its search efficiency, is termed mSAR. A set of experiments is applied to evaluate the performance of mSAR, solve the problem of multi-level thresholding for image segmentation, and demonstrate the impact of combining the OBL technique with the original SAR for improving solution quality and accelerating convergence speed. The effectiveness of the proposed mSAR is evaluated against other competing algorithms, including the L'evy flight distribution (LFD), Harris hawks optimization (HHO), sine cosine algorithm (SCA), equilibrium optimizer (EO), gravitational search algorithm (GSA), arithmetic optimization algorithm (AOA), and the original SAR. Furthermore, a set of experiments for multi-level thresholding image segmentation is performed to prove the superiority of the proposed mSAR using fuzzy entropy and the Otsu method as two objective functions over a set of benchmark images with different numbers of thresholds based on a set of evaluation matrices. Finally, analysis of the experiments' outcomes indicates that the mSAR algorithm is highly efficient in terms of the quality of the segmented image and feature conservation, compared with the other competing algorithms.
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Affiliation(s)
- Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
| | - Gaber M Mohamed
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Reem Alkanhel
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Ibrahim A Ibrahim
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
| | - Yaser M Wazery
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
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13
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Emam MM, El-Sattar HA, Houssein EH, Kamel S. Modified orca predation algorithm: developments and perspectives on global optimization and hybrid energy systems. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08492-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2023]
Abstract
AbstractThis paper provides a novel, unique, and improved optimization algorithm called the modified Orca Predation Algorithm (mOPA). The mOPA is based on the original Orca Predation Algorithm (OPA), which combines two enhancing strategies: Lévy flight and opposition-based learning. The mOPA method is proposed to enhance search efficiency and avoid the limitations of the original OPA. This mOPA method sets up to solve the global optimization issues. Additionally, its effectiveness is compared with various well-known metaheuristic methods, and the CEC’20 test suite challenges are used to illustrate how well the mOPA performs. Case analysis demonstrates that the proposed mOPA method outperforms the benchmark regarding computational speed and yields substantially higher performance than other methods. The mOPA is applied to ensure that all load demand is met with high reliability and the lowest energy cost of an isolated hybrid system. The optimal size of this hybrid system is determined through simulation and analysis in order to service a tiny distant location in Egypt while reducing costs. Photovoltaic panels, biomass gasifier, and fuel cell units compose the majority of this hybrid system’s configuration. To confirm the mOPA technique’s superiority, its outcomes have been compared with the original OPA and other well-known metaheuristic algorithms.
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14
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Torse DA, Khanai R, Pai K, Iyer S, Mavinkattimath S, Kallimani R, Shahpur S. Optimal feature selection for COVID-19 detection with CT images enabled by metaheuristic optimization and artificial intelligence. Multimed Tools Appl 2023:1-31. [PMID: 37362744 PMCID: PMC10025793 DOI: 10.1007/s11042-023-15031-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/09/2022] [Accepted: 02/27/2023] [Indexed: 06/28/2023]
Abstract
There is a broad range of novel Coronaviruses (CoV) such as the common cold, cough, and severe lung infections. The mutation of this virus, which originally started as COVID-19 in Wuhan, China, has continued the rapid spread globally. As the mutated form of this virus spreads across the world, testing and screening procedures of patients have become tedious for healthcare departments in largely populated countries such as India. To diagnose COVID-19 pneumonia by radiological methods, high-resolution computed tomography (CT) of the chest has been considered the most precise method of examination. The use of modern artificial intelligence (AI) techniques on chest high-resolution computed tomography (HRCT) images can help to detect the disease, especially in remote areas with a lack of specialized physicians. This article presents a novel metaheuristic algorithm for automatic COVID-19 detection using a least square support vector machine (LSSVM) classifier for three classes namely normal, COVID, and pneumonia. The proposed model results in a classification accuracy of 87.2% and an F1-score of 86.3% for multiclass classifications from simulations. The analysis of information transfer rate (ITR) revealed that the modified quantum-based marine predators algorithm (Mq-MPA) feature selection algorithm reduces the classification time of LSSVM by 23% when compared to the deep learning models.
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Affiliation(s)
- Dattaprasad A. Torse
- Department of ECE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Rajashri Khanai
- Department of CSE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Krishna Pai
- Department of ECE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Sridhar Iyer
- Department of ECE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Swati Mavinkattimath
- Department of ECE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Rakhee Kallimani
- Department of EEE, KLE Dr. M.S. Sheshgiri College of Engineering and Technology, Udyambag, Belagavi, KA 590008 India
| | - Salma Shahpur
- Department of ECE, Jain College of Engineering, Belagavi, KA 590008 India
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15
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S. K, Sudha L, Navaneetha Krishnan M. Water cycle tunicate swarm algorithm based deep residual network for virus detection with gene expression data. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2023. [DOI: 10.1080/21681163.2023.2165161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Affiliation(s)
- Karthi S.
- Department of Computer Science and Engineering, Annamalai University, Chidambaram, India
| | - L.R. Sudha
- Department of Computer Science and Engineering, Annamalai University, Chidambaram, India
| | - M. Navaneetha Krishnan
- Department of Computer Science and Engineering, St Joseph College of Engineering, Chennai, India
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16
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Hou S, Yu J, Su Y, Liu Z, Dai J. Flow distribution optimization of parallel pumps based on improved mayfly algorithm. IFS 2023. [DOI: 10.3233/jifs-222783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
An improved mayfly algorithm is proposed for the energy saving optimization of parallel chilled water pumps in central air conditioning system, with the minimum energy consumption of parallel pump units as the optimization objective and the speed ratio of each pump as the optimization variable for the solution. For the problem of uneven random initialization of mayflies, the variable definition method of Circle chaotic mapping is used to make the initial position of the population uniformly distributed in the solution space, and the mayfly fitness value and the optimal fitness value are incorporated into the calculation of the weight coefficient, which better balances the global exploration and local exploitation of the algorithm. For the problem that the algorithm is easy to fall into the local optimum at the later stage, a multi-subpopulation cooperative strategy is proposed to improve the global search ability of the algorithm. Finally, the performance of the improved mayfly algorithm is tested with two parallel pumping system cases, and the stability and time complexity of the algorithm are verified. The experiments show that the algorithm can get a better operation strategy in solving the parallel water pump energy saving optimization problem, and can achieve energy saving effect of 0.72% 8.68% compared with other optimization algorithms, and the convergence speed and stability of the algorithm have been significantly improved, which can be better applied to practical needs.
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Affiliation(s)
- Shuai Hou
- School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an, ShaanXi, China
| | - Junqi Yu
- School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an, ShaanXi, China
| | - Yucong Su
- School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an, ShaanXi, China
| | - Zongyi Liu
- School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an, ShaanXi, China
| | - Junwei Dai
- School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an, ShaanXi, China
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17
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Tallapragada VS, Manga NA, Kumar GP. A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images. Multimed Tools Appl 2023; 82:1-42. [PMID: 36712955 PMCID: PMC9859748 DOI: 10.1007/s11042-023-14367-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/22/2022] [Accepted: 01/02/2023] [Indexed: 06/18/2023]
Abstract
Recently, the Covid-19 pandemic has affected several lives of people globally, and there is a need for a massive number of screening tests to diagnose the existence of coronavirus. For the medical specialist, detecting COVID-19 cases is a difficult task. There is a need for fast, cheap and accurate diagnostic tools. The chest X-ray and the computerized tomography (CT) play a significant role in the COVID-19 diagnosis. The advancement of deep learning (DL) approaches helps to introduce a COVID diagnosis system to achieve maximum detection rate with minimum time complexity. This research proposed a discrete wavelet optimized network model for COVID-19 diagnosis and feature extraction to overcome these problems. It consists of three stages pre-processing, feature extraction and classification. The raw images are filtered in the pre-processing phase to eliminate unnecessary noises and improve the image quality using the MMG hybrid filtering technique. The next phase is feature extraction, in this stage, the features are extracted, and the dimensionality of the features is diminished with the aid of a modified discrete wavelet based Mobile Net model. The third stage is the classification here, the convolutional Aquila COVID detection network model is developed to classify normal and COVID-19 positive cases from the collected images of the COVID-CT and chest X-ray dataset. Finally, the performance of the proposed model is compared with some of the existing models in terms of accuracy, specificity, sensitivity, precision, f-score, negative predictive value (NPV) and positive predictive value (PPV), respectively. The proposed model achieves the performance of 99%, 100%, 98.5%, and 99.5% for the CT dataset, and the accomplished accuracy, specificity, sensitivity, and precision values of the proposed model for the X-ray dataset are 98%, 99%, 98% and 97% respectively. In addition, the statistical and cross validation analysis is conducted to validate the effectiveness of the proposed model.
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Affiliation(s)
| | | | - G.V. Pradeep Kumar
- Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, India
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18
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Han Y, Chen W, Heidari AA, Chen H. Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images. J Bionic Eng 2023; 20:1198-1262. [PMID: 36619872 PMCID: PMC9811903 DOI: 10.1007/s42235-022-00295-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 06/17/2023]
Abstract
Coronavirus Disease 2019 (COVID-19) is the most severe epidemic that is prevalent all over the world. How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic. Moreover, it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images. As we all know, image segmentation is a critical stage in image processing and analysis. To achieve better image segmentation results, this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO. Then utilizes RDMVO to calculate the maximum Kapur's entropy for multilevel threshold image segmentation. This image segmentation scheme is called RDMVO-MIS. We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS. First, RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions. Second, the image segmentation experiment was carried out using RDMVO-MIS, and some meta-heuristic algorithms were selected as comparisons. The test image dataset includes Berkeley images and COVID-19 Chest X-ray images. The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.
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Affiliation(s)
- Yan Han
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Weibin Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035 China
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19
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Ma BJ, Pereira JLJ, Oliva D, Liu S, Kuo YH. Manta ray foraging optimizer-based image segmentation with a two-strategy enhancement. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2022.110247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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20
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Wang G, Guo S, Han L, Zhao Z, Song X. COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm. Biomed Signal Process Control 2023; 79:104159. [PMID: 36119901 PMCID: PMC9464590 DOI: 10.1016/j.bspc.2022.104159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 08/10/2022] [Accepted: 09/04/2022] [Indexed: 11/17/2022]
Abstract
Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors.
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Affiliation(s)
- Guowei Wang
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Shuli Guo
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Lina Han
- Department of Cardiology, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Zhilei Zhao
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China
| | - Xiaowei Song
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China
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21
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Wang Z, Mo Y, Cui M, Hu J, Lyu Y. An improved golden jackal optimization for multilevel thresholding image segmentation. PLoS One 2023; 18:e0285211. [PMID: 37146052 PMCID: PMC10162520 DOI: 10.1371/journal.pone.0285211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/17/2023] [Indexed: 05/07/2023] Open
Abstract
Aerial photography is a long-range, non-contact method of target detection technology that enables qualitative or quantitative analysis of the target. However, aerial photography images generally have certain chromatic aberration and color distortion. Therefore, effective segmentation of aerial images can further enhance the feature information and reduce the computational difficulty for subsequent image processing. In this paper, we propose an improved version of Golden Jackal Optimization, which is dubbed Helper Mechanism Based Golden Jackal Optimization (HGJO), to apply multilevel threshold segmentation to aerial images. The proposed method uses opposition-based learning to boost population diversity. And a new approach to calculate the prey escape energy is proposed to improve the convergence speed of the algorithm. In addition, the Cauchy distribution is introduced to adjust the original update scheme to enhance the exploration capability of the algorithm. Finally, a novel "helper mechanism" is designed to improve the performance for escape the local optima. To demonstrate the effectiveness of the proposed algorithm, we use the CEC2022 benchmark function test suite to perform comparison experiments. the HGJO is compared with the original GJO and five classical meta-heuristics. The experimental results show that HGJO is able to achieve competitive results in the benchmark test set. Finally, all of the algorithms are applied to the experiments of variable threshold segmentation of aerial images, and the results show that the aerial photography images segmented by HGJO beat the others. Noteworthy, the source code of HGJO is publicly available at https://github.com/Vang-z/HGJO.
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Affiliation(s)
- Zihao Wang
- School of Artificial Intelligence, Guangxi Minzu University, Nanning, China
| | - Yuanbin Mo
- Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning, China
| | - Mingyue Cui
- School of Artificial Intelligence, Guangxi Minzu University, Nanning, China
| | - Jufeng Hu
- School of Artificial Intelligence, Guangxi Minzu University, Nanning, China
| | - Yucheng Lyu
- School of Artificial Intelligence, Guangxi Minzu University, Nanning, China
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22
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Emam MM, Houssein EH, Ghoniem RM. A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images. Comput Biol Med 2023; 152:106404. [PMID: 36521356 DOI: 10.1016/j.compbiomed.2022.106404] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 10/29/2022] [Accepted: 12/03/2022] [Indexed: 12/12/2022]
Abstract
In this paper, we proposed an enhanced reptile search algorithm (RSA) for global optimization and selected optimal thresholding values for multilevel image segmentation. RSA is a recent metaheuristic optimization algorithm depending on the hunting behavior of crocodiles. RSA is inclined to inadequate diversity, local optima, and unbalanced exploitation abilities as other metaheuristic algorithms. The RUNge Kutta optimizer (RUN) is a novel metaheuristic algorithm that has demonstrated effectiveness in solving real-world optimization problems. The enhanced solution quality (ESQ) in RUN utilizes the thus-far best solution to promote the quality of solutions, improve the convergence speed, and effectively balance the exploration and exploitation steps. Also, the Scale factor (SF) has a randomized adaptation nature, which helps RUN in further improving the exploration and exploitation steps. This parameter ensures a smooth transition from exploration to exploitation. In order to mitigate the drawbacks of the RSA algorithm, this paper proposed a modified RSA (mRSA), which combines the RSA algorithm with the RUN. The ESQ mechanism and the scale factor boost the original RSA's performance, enhance convergence speed, bypass local optimum, and enhance the balance between exploitation and exploration. The validity of mRSA was verified using two experimental sequences. First, we applied mRSA to CEC'2020 benchmark functions of various types and dimensions, showing that mRSA has more robust search capabilities than the original RSA and popular counterpart algorithms concerning statistical, convergence, and diversity measurements. The second experiment evaluated mRSA for a real-world application to solve magnetic resonance imaging (MRI) brain image segmentation. Overall experimental results confirm that the mRSA has a strong optimization ability. Also, mRSA method is a more successful multilevel thresholding segmentation and outperforms comparison methods according to different performance measures.
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Affiliation(s)
- Marwa M Emam
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Essam H Houssein
- Faculty of Computers and Information, Minia University, Minia, Egypt.
| | - Rania M Ghoniem
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; Department of Computer, Mansoura University, Mansoura, Egypt.
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23
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More PS, Saini BS. Competitive verse water wave optimisation enabled COVID-Net for COVID-19 detection from chest X-ray images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2022. [DOI: 10.1080/21681163.2022.2140074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
| | - Baljit Singh Saini
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, India
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24
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El-Haleem AMA, Anany MG, Abdelhakam MM, Mohamed NEDM, Elmesalawy MM. Geofencing-based Congestion Control in Workplaces Environment using Sequential Pattern Mining. 2022 4th Novel Intelligent and Leading Emerging Sciences Conference (NILES) 2022. [DOI: 10.1109/niles56402.2022.9942376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Ahmed M. Abd El-Haleem
- Helwan University,Faculty of Engineering,Department of Electronics and Communications Engineering,Cairo,Egypt
| | - Mohamed G. Anany
- Canadian International College (CIC),Electronics and Communications Engineering Department,Cairo,Egypt
| | - Mostafa M. Abdelhakam
- Helwan University,Faculty of Engineering,Department of Electronics and Communications Engineering,Cairo,Egypt
| | - Noor El-Deen M. Mohamed
- Helwan University,Faculty of Engineering,Computer and Systems Engineering Department,Cairo,Egypt
| | - Mahmoud M. Elmesalawy
- Helwan University,Faculty of Engineering,Department of Electronics and Communications Engineering,Cairo,Egypt
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25
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Gupta AU, Singh Bhadauria S, Liu J. Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC. Computational Intelligence and Neuroscience 2022; 2022:1-22. [PMID: 36317075 PMCID: PMC9617705 DOI: 10.1155/2022/4431817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/23/2022] [Accepted: 09/30/2022] [Indexed: 11/17/2022]
Abstract
During the COVID-19 pandemic, huge interstitial lung disease (ILD) lung images have been captured. It is high time to develop the efficient segmentation techniques utilized to separate the anatomical structures and ILD patterns for disease and infection level identification. The effectiveness of disease classification directly depends on the accuracy of initial stages like preprocessing and segmentation. This paper proposed a hybrid segmentation algorithm designed for ILD images by taking advantage of superpixel and K-means clustering approaches. Segmented superpixel images adapt the better irregular local and spatial neighborhoods that are helpful to improving the performance of K-means clustering-based ILD image segmentation. To overcome the limitations of multiclass belongings, semiadaptive wavelet-based fusion is applied over selected K-means clusters. The performance of the proposed SPFKMC was compared with that of 3-class Fuzzy C-Means clustering (FCM) and K-Means clustering in terms of accuracy, Jaccard similarity index, and Dice similarity coefficient. The SPFKMC algorithm gives an accuracy of 99.28%, DSC 98.72%, and JSI 97.87%. The proposed Fused Clustering gives better results as compared to traditional K-means clustering segmentation with wavelet-based fused cluster results.
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26
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Houssein EH, Abdelkareem DA, Emam MM, Hameed MA, Younan M. An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm. Comput Biol Med 2022; 149:106075. [PMID: 36115303 DOI: 10.1016/j.compbiomed.2022.106075] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/26/2022] [Accepted: 09/03/2022] [Indexed: 11/21/2022]
Abstract
Skin cancer is one of the worst cancers nowadays that poses a severe threat to the health and safety of individuals. Therefore, skin cancer classification and early diagnosis are recommended to preserve human life. Multilevel thresholding image segmentation is well-known and influential technique for extracting regions of interest from skin cancer images to improve the classification process. Therefore, this paper proposes an efficient version of the recently developed golden jackal optimization (GJO) algorithm, the opposition-based golden jackal optimizer (IGJO). The IGJO algorithm is used to solve the multilevel thresholding problem using Otsu's method as an objective function. The proposed algorithm is compared with seven other meta-heuristic algorithms: whale optimization algorithm, seagull optimization algorithm, salp swarm algorithm, Harris hawks optimization, artificial gorilla troops optimizer, marine predators' algorithms, and original GJO algorithm. The performance of the proposed algorithm is evaluated using four popular performance measures: peak signal-to-noise ratio, structure similarity index, feature similarity index, and mean square error. Experimental results show that the proposed algorithm outperforms other alternative algorithms in terms of PSNR, SSIM, FSIM, and MSE segmentation metrics and effectively resolves the segmentation problem.
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Houssein EH, Hassan MH, Kamel S, Hussain K, Hashim FA. Modified Lévy flight distribution algorithm for global optimization and parameters estimation of modified three-diode photovoltaic model. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03977-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
AbstractMany real-world problems demand optimization, minimization of costs and maximization of profits, and meta-heuristic algorithms have proficiently proved their ability to achieve optimum results. This study proposes an alternative algorithm of Lévy Flight Distribution (LFD) by integrating Opposition-based learning (OBL) operator, termed LFD-OBL, for resolving intrinsic drawbacks of the canonical LFD. The proposed approach adopts OBL operator for catering search stagnancy to ensure faster convergence rate. We validate the usefulness of our approach through IEEE CEC’20 test suite, and compare results with original LFD and several other counterparts such as Moth-flame optimization, whale optimization algorithm, grasshopper optimisation algorithm, thermal exchange optimization, sine-cosine algorithm, artificial ecosystem-based optimization, Henry gas solubility optimization, and Harris’ hawks optimization. To further validate the efficiency of LFD-OBL, we apply it on parameters optimization of Solar Cell based on the Three-Diode Photovoltaic model. The qualitative and quantitative results of all the experiments performed in this study suggest superiority of the proposed method.
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28
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Rizk-Allah RM, Zineldin MI, Mousa AAA, Abdel-Khalek S, Mohamed MS, Snášel V. On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery. INT J COMPUT INT SYS 2022. [PMCID: PMC9364860 DOI: 10.1007/s44196-022-00114-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon’s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.
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Affiliation(s)
- Rizk M. Rizk-Allah
- Basic Engineering Science Department, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511 Egypt
| | | | - Abd Allah A. Mousa
- Department of Mathematics, College of Science, Taif University, Taif, 21944 Saudi Arabia
| | - S. Abdel-Khalek
- Department of Mathematics, College of Science, Taif University, Taif, 21944 Saudi Arabia
| | - Mohamed S. Mohamed
- Department of Mathematics, College of Science, Taif University, Taif, 21944 Saudi Arabia
| | - Václav Snášel
- Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Poruba, 70800 Ostrava, Czech Republic
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Zhang R, Liu L. Distribution Network Regionalized Fault Location Based on an Improved Manta Ray Foraging Optimization Algorithm. Electronics 2022; 11:2342. [DOI: 10.3390/electronics11152342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
To address the problem that the accuracy of traditional intelligent algorithms in distribution network fault location decreases with the expansion of distribution network scale, a regionalized fault location method for distribution networks containing distributed power sources based on the improved manta ray foraging optimization (IMRFO) algorithm is proposed. First, the global convergence property of the basic manta ray foraging optimization (MRFO) algorithm is improved by fusing the restart strategy and the opposition-based learning strategy. Then, based on the two-port equivalence principle, a topological model for regionalized fault hierarchical localization in distribution networks is constructed. Finally, the algorithm is improved by binary discretization using the Sigmoid function to output the fault vector and complete the fault location of the distribution network. Simulation experiments are conducted using MATLAB for IEEE-33 node distribution networks and the simulation results show that the IMRFO algorithm combined with the regionalization of complex distribution networks has a better effect of dimensionality reduction. Compared with the traditional distribution network simulation model, the fault location fault tolerance is greatly improved and its accuracy rate is increased by 1.8% and the location speed is improved by 15.537 ms.
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Houssein EH, Emam MM, Ali AA. An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm. Neural Comput Appl 2022;:1-19. [PMID: 35698722 DOI: 10.1007/s00521-022-07445-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 05/14/2022] [Indexed: 11/12/2022]
Abstract
Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.
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31
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Zhu D, Xie L, Zhou C, Oliva D. K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm. Computational Intelligence and Neuroscience 2022; 2022:1-26. [PMID: 35341174 PMCID: PMC8942626 DOI: 10.1155/2022/4587880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/03/2022] [Accepted: 02/08/2022] [Indexed: 11/27/2022]
Abstract
Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.
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32
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Premalatha R, Dhanalakshmi P. Enhancement and segmentation of medical images through pythagorean fuzzy sets-An innovative approach. Neural Comput Appl 2022; 34:11553-11569. [PMID: 35250182 PMCID: PMC8889401 DOI: 10.1007/s00521-022-07043-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 01/30/2022] [Indexed: 11/16/2022]
Abstract
Image segmentation has attracted a lot of attention due to its potential biomedical applications. Based on these, in the current research, an attempt has been made to explore object enhancement and segmentation for CT images of lungs infected with COVID-19. By implementing Pythagorean fuzzy entropy, the considered images were enhanced. Further, by constructing Pythagorean fuzzy measures and utilizing the thresholding technique, the required values of thresholds for the segmentation of the proposed scheme are assessed. The object extraction ability of the five segmentation algorithms including current sophisticated, and proposed schemes are evaluated by applying the quality measurement factors. Ultimately, the proposed scheme has the best effect on object separation as well as the quality measurement values.
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33
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Rai R, Das A, Dhal KG. Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review. Evol Syst (Berl) 2022; 13:889-945. [PMID: 37520044 PMCID: PMC8859498 DOI: 10.1007/s12530-022-09425-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 01/15/2022] [Indexed: 12/14/2022]
Abstract
Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.
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Affiliation(s)
- Rebika Rai
- Department of Computer Applications, Sikkim University, Sikkim, India
| | - Arunita Das
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal India
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34
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Irene D S, Beulah JR, K. A, K. K. An efficient COVID-19 detection from CT images using ensemble support vector machine with Ludo game-based swarm optimisation. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2022. [DOI: 10.1080/21681163.2021.2024088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Shiny Irene D
- Department of Computing Technologies, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, TN, India
| | - J. Rene Beulah
- Department of Computing Technologies, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, TN, India
| | - Anitha K.
- Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha nagar, Thandalam, Chennai, Tamil Nadu, India
| | - Kannan K.
- Department of Electronics and Communication Engineering, R.M.K College of Engineering And Technology, R.S.M. Nagar, Puduvoyal, Gummidipoondi (TK), Thiruvallur (DT), Tamilnadu, India
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35
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Nama S. A novel improved SMA with quasi reflection operator: Performance analysis, application to the image segmentation problem of Covid-19 chest X-ray images. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108483] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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36
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Toğaçar M. Disease type detection in lung and colon cancer images using the complement approach of inefficient sets. Comput Biol Med 2021; 137:104827. [PMID: 34560401 DOI: 10.1016/j.compbiomed.2021.104827] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/25/2021] [Accepted: 08/29/2021] [Indexed: 12/19/2022]
Abstract
Lung and colon cancers are deadly diseases that can develop simultaneously in organs and adversely affect human life in some special cases. Although the frequency of simultaneous occurrence of these two types of cancer is unlikely, there is a high probability of metastasis between the two organs if not diagnosed early. Traditionally, specialists have to go through a lengthy and complicated process to examine histopathological images and diagnose cancer cases; yet, it is now possible to achieve this process faster with the available technological possibilities. In this study, artificial intelligence-supported model and optimization methods were used to realize the classification of lung and colon cancers' histopathological images. The used dataset has five classes of histopathological images consisting of two colon cancer classes and three lung cancer classes. In the proposed approach, the image classes were trained from scratch with the DarkNet-19 model, which is one of the deep learning models. In the feature set extracted from the DarkNet-19 model, selection of the inefficient features was performed by using Equilibrium and Manta Ray Foraging optimization algorithms. Then, the set containing the inefficient features was distinguished from the rest of the set features, creating an efficient feature set (complementary rule insets). The efficient features obtained by the two used optimization algorithms were combined and classified with the Support Vector Machine (SVM) method. The overall accuracy rate obtained in the classification process was 99.69%. Based on the outcomes of this study, it has been observed that using the complementary method together with some optimization methods improved the classification performance of the dataset.
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Affiliation(s)
- Mesut Toğaçar
- Department of Computer Technology, Technical Sciences Vocational School, Fırat UniversityElazig, Turkey.
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37
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Sharafeldeen A, Elsharkawy M, Alghamdi NS, Soliman A, El-Baz A. Precise Segmentation of COVID-19 Infected Lung from CT Images Based on Adaptive First-Order Appearance Model with Morphological/Anatomical Constraints. Sensors (Basel) 2021; 21:5482. [PMID: 34450923 PMCID: PMC8399192 DOI: 10.3390/s21165482] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/08/2021] [Accepted: 08/10/2021] [Indexed: 12/16/2022]
Abstract
A new segmentation technique is introduced for delineating the lung region in 3D computed tomography (CT) images. To accurately model the distribution of Hounsfield scale values within both chest and lung regions, a new probabilistic model is developed that depends on a linear combination of Gaussian (LCG). Moreover, we modified the conventional expectation-maximization (EM) algorithm to be run in a sequential way to estimate both the dominant Gaussian components (one for the lung region and one for the chest region) and the subdominant Gaussian components, which are used to refine the final estimated joint density. To estimate the marginal density from the mixed density, a modified k-means clustering approach is employed to classify the Gaussian subdominant components to determine which components belong properly to a lung and which components belong to a chest. The initial segmentation, based on the LCG-model, is then refined by the imposition of 3D morphological constraints based on a 3D Markov-Gibbs random field (MGRF) with analytically estimated potentials. The proposed approach was tested on CT data from 32 coronavirus disease 2019 (COVID-19) patients. Segmentation quality was quantitatively evaluated using four metrics: Dice similarity coefficient (DSC), overlap coefficient, 95th-percentile bidirectional Hausdorff distance (BHD), and absolute lung volume difference (ALVD), and it achieved 95.67±1.83%, 91.76±3.29%, 4.86±5.01, and 2.93±2.39, respectively. The reported results showed the capability of the proposed approach to accurately segment healthy lung tissues in addition to pathological lung tissues caused by COVID-19, outperforming four current, state-of-the-art deep learning-based lung segmentation approaches.
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Affiliation(s)
- Ahmed Sharafeldeen
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (M.E.); (A.S.)
| | - Mohamed Elsharkawy
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (M.E.); (A.S.)
| | - Norah Saleh Alghamdi
- College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia
| | - Ahmed Soliman
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (M.E.); (A.S.)
| | - Ayman El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.S.); (M.E.); (A.S.)
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