  Brain tumors, particularly glioblastoma, continue to challenge medical
diagnostics and treatments globally. This paper explores the application of
deep learning to multi-modality magnetic resonance imaging (MRI) data for
enhanced brain tumor segmentation precision in the Sub-Saharan Africa patient
population. We introduce an ensemble method that comprises eleven unique
variations based on three core architectures: UNet3D, ONet3D, SphereNet3D and
modified loss functions. The study emphasizes the need for both age- and
population-based segmentation models, to fully account for the complexities in
the brain. Our findings reveal that the ensemble approach, combining different
architectures, outperforms single models, leading to improved evaluation
metrics. Specifically, the results exhibit Dice scores of 0.82, 0.82, and 0.87
for enhancing tumor, tumor core, and whole tumor labels respectively. These
results underline the potential of tailored deep learning techniques in
precisely segmenting brain tumors and lay groundwork for future work to
fine-tune models and assess performance across different brain regions.
