Automatic segmentation of brain tumors from MRI scans is a critical task at the intersection of medical imaging and deep learning. This paper investigates two multimodal fusion strategies for adapting the TransResU-Net architecture to multi-sequence MRI input: early fusion, where modalities are concatenated at the input level, and intermediate fusion, where each modality is processed by a dedicated ResNet50 encoder before feature-level integration. Both strategies are evaluated on three publicly available datasets that cover different pathological contexts: BrainMetShare (brain metastases), REMBRANDT (adult primary tumors), and BraTS-PED (pediatric primary tumors). The results show that multimodal models consistently outperform unimodal baselines, with Dice score gains of up to 6.1 percentage points. Among individual modalities, FLAIR and T2 emerge as the most informative for whole-tumor binary segmentation. The FLAIR+T2 early fusion configuration achieves competitive or superior results compared to using all four modalities, suggesting that a well-selected subset of modalities can offer a favorable trade-off between performance and computational cost. Intermediate fusion yields the best overall result on BrainMetShare (Dice 83.6%), but does not consistently outperform early fusion on the other datasets, indicating that the benefit of dedicated per-modality encoders is conditioned on dataset characteristics.