A Novel Deep Learning Approach for MRI Segmentation of Brain Tumors

This study introduces a novel deep learning model, BTU-Net, designed to segment brain tumors from magnetic resonance imaging (MRI) scans. The lack of accuracy and inefficiency of traditional techniques used for brain tumor segmentation in MRI scans emphasize the need for advanced segmentation methods. BTU-Net is designed with unique feature extraction blocks and fusion blocks to efficiently handle the multi-scale features of brain tumors. In addition, an innovative attention mechanism is employed to enable the network to focus on the most information-rich regions, thus improving accuracy, especially for small or complex tumor structures. The model is trained and validated on a public dataset using appropriate optimization techniques and loss functions. Comparative evaluation shows that BTU-Net outperforms existing models such as U-Net, Deeplabv3, and MA-Net in terms of segmentation performance. This study lays the foundation for potential integration of BTU-Net in clinical practice, which may help improve the diagnosis of brain tumors.