Bayesian Optimization-Driven U-Net Architecture Tuning for Brain Tumor Segmentation
Shoffan Saifullah, Rafał Dreżewski · 2026
Precise brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for clinical diagnosis and treatment planning. However, determining an optimal deep learning architecture for such tasks remains a challenge due to the vast hyperparameter space and structural variations. This paper presents a novel approach that integrates Bayesian Optimization (BO) to automatically tune the U-Net architecture for effective brain tumor segmentation. The proposed BO-UNet framework searches over encoder, bottleneck, and decoder configurations using a Gaussian Process-based surrogate model, guided by a fitness function derived from Dice Similarity Coefficient (DSC) and Jaccard Index (JI). Experiments were conducted on two benchmark datasets: the Figshare Brain Tumor Segmentation (FBTS) dataset and the BraTS 2021 dataset (focused on Whole Tumor segmentation). The best-discovered architecture [64, 64, 64, 256, 64, 128, 256] achieved notable performance: on the FBTS dataset, it reached 0.9503 DSC and 0.9054 JI; on BraTS 2021, it obtained 0.9261 DSC and 0.8631 JI, outperforming several state-of-the-art methods. Convergence and segmentation-map evolution confirm that BO effectively guided the architectural search process. These findings demonstrate the potential of BO-driven deep learning in medical imaging, opening new avenues for architecture-level optimization with minimal manual intervention.