Brain tumors are among the most lethal diseases in the nervous system, and the accuracy of their diagnosis and treatment directly impacts patient prognosis and quality of life. Convolutional Neural Networks demonstrate efficient feature learning capabilities and high segmentation precision in image feature extraction and segmentation, providing an efficient solution for fast brain tumor segmentation. To address challenges in brain tumor MRI image segmentation, such as blurred boundaries, low contrast between tumors and normal tissue, and background noise, we integrate residual blocks and attention mechanisms into the traditional U-Net architecture to develop an enhanced U-Net model based on residual attention. The model is trained using a public dataset, optimized jointly with the Sigmoid activation function and binary cross entropy loss function to alleviate class imbalance between tumor and normal tissue. After multiple rounds of iterative optimization, the model achieves segmentation validation on test MRI images, with results closely matching ground truth labels and excellent performance across quantitative metrics. Ablation experiments demonstrate that the residual attention U-Net outperforms traditional U-Net, residual U-Net, attention U-Net in terms of Dice, IoU, and Recall metrics, confirming the model's effectiveness and practical value in brain tumor MRI image segmentation tasks.
Key words
brain tumor /
medical image segmentation /
U-Net model /
residual block /
attention mechanism
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