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

Overview

1. Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia, su.edu.sa
2. Innov′COM Laboratory-Sup′Com, University of Carthage, Tunis, Tunisia, ucar.rnu.tn

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.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/masoudnickparvar, at Kaggle; found in the references
• kaggle.com/datasets/mateuszbuda, at Kaggle; found in “Data Availability Statement”

Data Availability Statement

The data are openly available in a public repository (https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation/data).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

• Issue: n/a → 1

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 6 keywords, 34 references.

Cite

This paper

Alkahtani, A. M., & Dardouri, S. (2026). Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis. International journal of biomedical imaging, 2026(1), 5318118. https://doi.org/10.1155/ijbi/5318118

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