Objective
This study aims to develop an efficient and stable medical image classification framework that enhances feature extraction in high-dimensional spaces and improves training stability in traditional neural network models. The ultimate goal is to improve the accuracy and generalization capability of medical image classification.Methods: A novel classification framework integrating Particle Swarm Optimization (PSO) with Fractional-order Principal Component Analysis (FPCA) is proposed. PSO is employed to adaptively optimize the fractional-order parameter of FPCA, thereby enhancing its ability to extract discriminative features from medical images. Additionally, an improved Sigmoid activation function is incorporated into a backpropagation (BP) neural network to improve output scaling behavior and training stability. The proposed method is evaluated on three datasets: MRI brain tumor images, COVID-19 chest X-rays, and retinal vascular images.Results: Experimental results demonstrate that the proposed approach achieves higher classification accuracy across all three benchmark datasets compared to traditional PCA, fixed-order FPCA, and standard BP neural networks. The improved activation function contributes to stable training behavior, while PSO-based optimization strengthens the robustness and generalization of the feature extraction process.Conclusion: The PSO-enhanced FPCA combined with an improved BP neural network provides an effective framework for medical image classification. The method exhibits promising generalization performance and superior classification accuracy, highlighting its potential for intelligent medical image diagnosis applications.
