Accurate brain tumor segmentation in magnetic resonance imaging (MRI) remains a challenging task due to the high variability in tumor appearance, shape, and location. Manual segmentation is time‐consuming, subjective, and impractical for large‐scale clinical use, highlighting the need for robust automated solutions. This study introduces an enhanced U‐Net architecture with a ResNet50 encoder, designed to improve feature extraction through deeper convolutional layers and residual connections. By reformulating tumor delineation as a pixel‐level segmentation problem rather than image‐level classification, the model achieves more precise boundary detection. Trained on the publicly available TCGA‐LGG dataset, the proposed model significantly outperformed the baseline U‐Net, achieving a Dice score of 0.9659, an Intersection over Union (IoU) of 0.9567, and a Matthews correlation coefficient (MCC) of 0.9253. These results demonstrate superior segmentation capability compared to standard U‐Net and are competitive with recent state‐of‐the‐art methods. The findings highlight the potential of the proposed framework as a proof of concept for integration into clinical decision support, while also underscoring the need for future validation on larger, multi‐institutional datasets.
Alkahtani et al. (Thu,) studied this question.