NeXtBrain addresses the challenge of classifying brain tumors from MRI scans by combining two complementary strategies: local texture recognition from convolutional networks and global context modeling from transformers. The authors design a hybrid architecture with custom “NeXt” blocks, one convolutional and one transformer-based, to capture both fine-grained details such as edges and textures as well as long-range anatomical relationships. They test the model on two public MRI datasets, compare performance against well-known baselines, and use ablation studies to show how each component contributes to the overall results. The reasoning is clear: accurate tumor classification requires attention to both local detail and broader structural patterns. Their experiments show that the hybrid model consistently outperforms traditional CNNs and pure transformer models while keeping the parameter count and computational demands manageable. The paper highlights that generalization across different datasets is as important as accuracy on a single test set, and it acknowledges open challenges such as dataset diversity, cross-site variability, three-dimensional imaging, and clinical interpretability. Overall, the study makes a careful case that blending local and global feature learning can improve brain tumor classification while remaining computationally efficient.