Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.

Abstract

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.