Adaptive fractional-order Pulse-Coupled Neural Networks with multi-scale optimization for Skin Image Segmentation

Abstract

This study presents a novel image segmentation method termed Fractional Coati Optimization Algorithm-Pulse-Coupled Neural Network (FCOA-PCNN), which synergistically integrates a Fractional Coati Optimization Algorithm (FCOA) with a Pulse-Coupled Neural Network (PCNN). FCOA introduces a fractional-order calculus mechanism into the original Coati Optimization Algorithm (COA), leveraging its inherent memory characteristics to enhance global search capability and convergence speed. Furthermore, an adaptive order control strategy is proposed, enabling dynamic adjustment of the fractional order during iterations to improve robustness and optimization efficiency. To optimize the key parameters of the PCNN, researchers construct a composite fitness function based on image information entropy and edge matching metrics, effectively capturing both global structure and local edge features. Experimental results from the CEC2005 benchmark suite demonstrate FCOA’s superior optimization performance over state-of-the-art algorithms in terms of convergence precision and stability. Moreover, extensive evaluations on the ISIC 2016 skin lesion dataset validate the superior segmentation performance of FCOA-PCNN, which achieved a Dice Coefficient of 92.01% and a Jaccard Index of 85.40%, outperforming both deep learning-based and traditional segmentation methods. Ablation studies further confirm the critical role of fractional-order components in enhancing the segmentation accuracy and model robustness. These findings highlight the potential of FCOA-PCNN as an effective and efficient tool for medical image segmentation applications.