ORIGINAL RESEARCH article
Front. Imaging
Sec. Image Retrieval and Analysis
This article is part of the Research TopicTransforming medical imaging with advanced deep learning techniquesView all 6 articles
Enhancement of multiobjective Darwinian particle swarm optimization for neural-network-based multimodal medical image fusion
Provisionally accepted
- University of Nigeria, Nsukka, Nsukka, Nigeria
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The purpose of this research is to develop a multimodal medical image fusion method that will give high-performance fusion images, at a speed high enough for efficient real-time image-guided surgeries. This paper, therefore proposes an improved multi-objective Darwinian particle swarm optimization method that incorporates a fractional calculus operator for effective multimodal medical image fusion. This is because multimodal medical image fusion is essential in many clinical diagnoses, and it represents a multiobjective problem due to the important objective indicators for measuring its efficiencies, such as the parameters of the neural network and the speed of the fusion process. The proposed method aims to optimize the Tsallis cross-entropy as a stimulating input to the pulse-coupled neural network (PCNN) for multimodal image fusion. In this work, multiobjective Darwinian particle swarm optimization (MODPSO) is utilized due to its ability to escape local optima more effectively than classical MOPSO. The approach used is that the convergence rate of MODPSO is improved by introducing a fractional calculus operator, which is incorporated into the updating formulas of the velocity and position of the particles. The output of the PCNN serves as an optimal parameter for fusing the high-frequency coefficients of decomposed source images, which are initially decomposed into low-and high-frequency subbands. The low-frequency coefficients are fused using an averaging method. Results obtained in this paper show that the proposed method yields the highest average accuracy of 90.7% after a 3-fold cross-validation was carried out with a small dataset extracted from a larger available dataset. In conclusion, experimental results demonstrate the superiority of the proposed method over comparative methods in terms of both visual quality and quantitative evaluation.
Keywords: Fractional-order Darwinian particle swarm optimization, Medical image fusion, multi-objective optimization, Pulse coupled neural networks, Tsallis function
Received: 23 Nov 2025; Accepted: 02 Feb 2026.
Copyright: © 2026 Ogbuanya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chisom Ezinne Ogbuanya
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