Brain tumor segmentation using particle swarm optimized histogram equalization and a VGG19 based U-Net

Abstract

Abstract Accurate brain tumor segmentation in MRI scans is critical for diagnosis and treatment planning. Conventional methods are often time-consuming, error-prone, and subject to operator bias. Although recent deep-learning approaches show promise for automating medical image segmentation, challenges such as interpretability hinder their clinical adoption. To address these issues, we propose SPOHE-VGGNet, a novel segmentation model that integrates a modified U-Net architecture with VGG-19 and Particle Swarm Optimized Histogram Equalization (PSO-HE). This approach enhances MRI contrast and achieves state-of-the-art segmentation accuracy. PSO-HE outperforms traditional and PSO-based image enhancement techniques, delivering superior image quality and facilitating precise feature extraction by VGG-19. We evaluated SPOHE-VGGNet on the Figshare T1-CE MRI dataset (3,064 images encompassing meningioma, glioma, and pituitary tumor classes) and the multi-modal BraTS 2021 dataset (1,251 images across four MRI modalities focusing on whole tumor masks). On the Figshare dataset, the model achieved Dice Similarity Coefficients (DSC) of 0.9522 for meningioma, 0.9428 for glioma, and 0.9518 for pituitary tumors. For whole tumor segmentation on the BraTS dataset, it attained a DSC of 0.9463. Performance metrics for Figshare included accuracy, precision, recall, and F1-score of 0.9989, 0.9680, 0.9647, and 0.9662, respectively. On the BraTS dataset, these metrics were 0.9977, 0.9544, 0.9738, and 0.9640, underscoring the model’s robustness across different imaging modalities. By combining high performance with interpretability, SPOHE-VGGNet provides a reliable and accurate tool for brain tumor segmentation, advancing clinical decision-making.

The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗