A Model for Image Segmentation under Noisy Conditions and Anomaly Detection in Medical MRI Images Using an RBF Neural Network and the PSO Metaheuristic Algorithm for Breast Cancer Detection - Maan Turki AL Khafaji, Ehab; Moradi, Hussein; Khalid Obayes, Hadab; Nasri, Mehdi; Khorsand, Reihaneh (Management Strategies and Engineering Sciences, 2027)
The aim of the present study was to develop an intelligent model for image segmentation under noisy conditions and anomaly detection in medical MRI images using a Radial Basis Function (RBF) neural network and the Particle Swarm Optimization (PSO) metaheuristic algorithm. In this model, the RBF neural network was responsible for learning and separating different image regions, while the PSO algorithm was employed for the automatic optimization of the network parameters, including centers, weights, and radii of the basis functions. The MRI images used in this study consisted of real data from the breast tissues of patients with and without cancer. Following the preprocessing stage, which included Gaussian and salt-and-pepper noise removal and pixel intensity normalization, the images were applied to the proposed model. The model output provided accurate segmentation of regions suspected of abnormalities from other tissues, and the results were compared with classical K-Means, FCM, and standalone RBF network methods. The findings demonstrated that the hybrid RBF–PSO model was able to increase segmentation accuracy under noisy conditions to an average of 96.4% and achieved a Dice overlap index of 0.92, representing a significant improvement compared to the other methods. Furthermore, the proposed model showed stable performance in preserving the actual tumor boundaries and eliminating noisy regions. Based on the obtained results, the RBF–PSO model can be considered an efficient tool for the early detection of breast cancer through the automated analysis of MRI images. It is recommended that future studies integrate this model with deep learning networks in order to improve its accuracy and generalizability in clinical datasets.