Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement
Metaheuristic optimization algorithms (MOAs) are popularly deployed for medical image enhancement (MIE) purposes. However, with an ever-increasing rate of newer MOAs being proposed in the literature, the question arises as to whether there exist any significant advantage(s) among these different MOA...
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| Format: | Article |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9732433/ |
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| author | Muhtahir O. Oloyede Adeiza J. Onumanyi Habeeb Bello-Salau Karim Djouani Anish Kurien |
| author_facet | Muhtahir O. Oloyede Adeiza J. Onumanyi Habeeb Bello-Salau Karim Djouani Anish Kurien |
| author_sort | Muhtahir O. Oloyede |
| collection | DOAJ |
| description | Metaheuristic optimization algorithms (MOAs) are popularly deployed for medical image enhancement (MIE) purposes. However, with an ever-increasing rate of newer MOAs being proposed in the literature, the question arises as to whether there exist any significant advantage(s) among these different MOAs, particularly as it pertains to MIE. In this paper, we explore this question by analyzing nine well-known MOAs for MIE, namely the artificial bee colony, cuckoo search, differential evolution, firefly, genetic algorithm, particle swarm optimization (PSO), covariance matrix adaptive evolutionary strategy (CMAES), whale optimization algorithm (WOA), and the grey wolf optimization (GWO) algorithms. First, instead of measuring an MOA’s performance based on the number of generations, we adopted the fitness computation rate (FCR), which enables MOAs to be compared in a fairer sense. Secondly, we used a combination of a well-known transformation function and a robust evaluation function as our objective function in the MOAs considered in our study. Then, medical images were obtained from the Medpix database with representative samples selected from across the different parts of the body for MIE evaluation purposes. Within the constraints of the datasets used, the results indicate that, while the GWO and WOA algorithms performed slightly better empirically than the other methods over an average of 1000 Monte Carlo trials, there was little/no statistical significant difference between the other methods. The timing performance also demonstrates that there was no significant difference in the real-time processing speeds of the various MOAs, particularly when evaluated under the same FCR. As a consequence, preliminary findings from our study suggest that employing a range of past and current MOAs or proposing newer MOAs for MIE may not necessarily guarantee substantial comparative enhancement benefits. This might suggest that under high FCR levels, any MOA can be utilized for MIE. |
| first_indexed | 2024-12-20T23:02:39Z |
| format | Article |
| id | doaj.art-c2cc28e3277345f1af9af00e1ccd53c1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T23:02:39Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c2cc28e3277345f1af9af00e1ccd53c12022-12-21T19:23:58ZengIEEEIEEE Access2169-35362022-01-0110280142803610.1109/ACCESS.2022.31583249732433Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image EnhancementMuhtahir O. Oloyede0https://orcid.org/0000-0001-9147-0179Adeiza J. Onumanyi1Habeeb Bello-Salau2https://orcid.org/0000-0001-9207-8670Karim Djouani3Anish Kurien4https://orcid.org/0000-0002-7250-3665F’SATI/Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South AfricaAdvanced Internet of Things, Next Generation Enterprises and Institutions, Council for Scientific and Industrial Research (CSIR), Pretoria, South AfricaDepartment of Computer Engineering, Ahmadu Bello University, Zaria, NigeriaF’SATI/Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South AfricaF’SATI/Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South AfricaMetaheuristic optimization algorithms (MOAs) are popularly deployed for medical image enhancement (MIE) purposes. However, with an ever-increasing rate of newer MOAs being proposed in the literature, the question arises as to whether there exist any significant advantage(s) among these different MOAs, particularly as it pertains to MIE. In this paper, we explore this question by analyzing nine well-known MOAs for MIE, namely the artificial bee colony, cuckoo search, differential evolution, firefly, genetic algorithm, particle swarm optimization (PSO), covariance matrix adaptive evolutionary strategy (CMAES), whale optimization algorithm (WOA), and the grey wolf optimization (GWO) algorithms. First, instead of measuring an MOA’s performance based on the number of generations, we adopted the fitness computation rate (FCR), which enables MOAs to be compared in a fairer sense. Secondly, we used a combination of a well-known transformation function and a robust evaluation function as our objective function in the MOAs considered in our study. Then, medical images were obtained from the Medpix database with representative samples selected from across the different parts of the body for MIE evaluation purposes. Within the constraints of the datasets used, the results indicate that, while the GWO and WOA algorithms performed slightly better empirically than the other methods over an average of 1000 Monte Carlo trials, there was little/no statistical significant difference between the other methods. The timing performance also demonstrates that there was no significant difference in the real-time processing speeds of the various MOAs, particularly when evaluated under the same FCR. As a consequence, preliminary findings from our study suggest that employing a range of past and current MOAs or proposing newer MOAs for MIE may not necessarily guarantee substantial comparative enhancement benefits. This might suggest that under high FCR levels, any MOA can be utilized for MIE.https://ieeexplore.ieee.org/document/9732433/Comparisonimagesmetaheuristicoptimizationperformance |
| spellingShingle | Muhtahir O. Oloyede Adeiza J. Onumanyi Habeeb Bello-Salau Karim Djouani Anish Kurien Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement IEEE Access Comparison images metaheuristic optimization performance |
| title | Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement |
| title_full | Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement |
| title_fullStr | Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement |
| title_full_unstemmed | Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement |
| title_short | Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement |
| title_sort | exploratory analysis of different metaheuristic optimization methods for medical image enhancement |
| topic | Comparison images metaheuristic optimization performance |
| url | https://ieeexplore.ieee.org/document/9732433/ |
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