Exploratory analysis of different metaheuristic optimization methods for medical image enhancement.
| dc.contributor.author | Oloyede, Muhtahir O. | |
| dc.contributor.author | Onumanyi, Adeiza J. | |
| dc.contributor.author | Bello-Salau, Habeeb | |
| dc.contributor.author | Djouani, Karim | |
| dc.contributor.author | Kurien, Anish | |
| dc.date.accessioned | 2025-07-29T06:20:48Z | |
| dc.date.available | 2025-07-29T06:20:48Z | |
| dc.date.issued | 2022-03-05 | |
| dc.description.abstract | 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. | |
| dc.format | 28014-28036 Pages | |
| dc.identifier.doi | http://10.1109/ACCESS.2022.3158324 | |
| dc.identifier.issn | 2169-3536 (E) | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14519/1930 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers | |
| dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.source.journaltitle | IEEE Access | |
| dc.subject | Comparison | |
| dc.subject | Images | |
| dc.subject | Metaheuristic | |
| dc.subject | Optimization | |
| dc.subject | Performance | |
| dc.title | Exploratory analysis of different metaheuristic optimization methods for medical image enhancement. | |
| dc.type | Article |