Enhancement of multi-objective Darwinian particle swarm optimization for neural-network-based multimodal medical image fusion

The purpose of this research is to develop an efficient multimodal medical image fusion method. 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. This work utilizes multiobjective Darwinian particle swarm optimization (MODPSO) 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. 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 visual quality and quantitative evaluation.