Bayesian Optimization-Driven U-Net Architecture Tuning for Brain Tumor Segmentation

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.