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

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 improves segmentation performance relative to the evaluated preprocessing baselines. 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 BraTS 2021 dataset comprising 1,251 cases, with four MRI modalities processed independently for two-dimensional Whole Tumor segmentation. 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, demonstrating its applicability to independently evaluated two-dimensional slices from different MRI modalities. By combining segmentation performance with interpretability analyses, SPOHE-VGGNet provides a research-oriented framework for two-dimensional brain tumor MRI segmentation. Further external, volumetric, and clinician-centered validation is required before its use in clinical decision-making can be considered.