Accurate and robust brain tumor segmentation remains a critical challenge in medical image analysis due to high inter-patient variability, complex tumor morphology, and modality-specific noise in MRI scans. This study proposes PSO-GA-U-Net, a novel hybrid deep learning framework that integrates Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) to optimize the U-Net architecture, enhancing segmentation performance and generalization. PSO dynamically tunes the learning rate to accommodate modality-specific variations, while the GA adaptively regulates dropout to improve feature diversity and reduce overfitting. The model was evaluated on three benchmark datasets—FBTS, BraTS 2021, and BraTS 2018—using five-fold cross-validation. PSO-GA-U-Net achieves Dice Similarity Coefficients (DSC) of 0.9587, 0.9406, and 0.9480 and Jaccard Index (JI) scores of 0.9209, 0.8881, and 0.9024, respectively, consistently outperforming state-of-the-art models in both overlap accuracy and boundary delineation. Statistical tests confirm that these improvements are significant across folds (p<0.05). Visual heatmaps further illustrate the model’s ability to preserve structural integrity across tumor types and modalities. These results indicate that metaheuristic-guided deep learning offers a promising and clinically applicable solution for automatic tumor segmentation in radiological workflows.
Saifullah et al. (Sat,) studied this question.