How are Deep Learning Models Used in Brain Tumor Segmentation?
Deep learning models, particularly CNNs, have shown great promise in brain tumor segmentation. They are trained on large datasets of annotated images and can learn intricate patterns that distinguish tumors from normal tissues. Techniques such as U-Net and ResNet architectures are widely used. These models typically involve: - Preprocessing: Normalizing and augmenting data to improve model robustness. - Training: Using annotated datasets to train the model. - Validation and Testing: Evaluating the model's performance on unseen data to ensure generalizability.
