Deep Learning-Based Medical Image Segmentation for Brain Tumors

Yudian Pan

2025

Abstract

Brain tumors present a considerable health challenge, dramatically impacting both survival and quality of life. This study introduces a improved deep learning approach for segmenting brain tumors in MRI scans, intending to overcome the constraints of those existing approaches. The proposed model builds upon the conventional U-Net architecture by incorporating the Convolutional Block Attention Module (CBAM), designed to enhance the feature extraction capabilities. By integrating both channel-wise and spatial attention mechanisms, this approach emphasizes relevant tumor regions while preserving structural detail. Experiments evaluations on the TCGA Brain Tumor MRI dataset confirm the remarkable advantages of our UNet+CBAM model compared to baseline approaches, achieving a Dice coefficient of 0.936 and an IoU of 0.882. This proposed model successfully captures tumor boundaries with high precision and provides detailed segmentation maps that could assist clinical diagnosis. While acknowledging the challenges posed by computational complexity, this study makes a significant contribution to the advancement of automated brain tumor segmentation technology, which holds considerable potential for practical applications in medical settings. Subsequent studies will prioritize the optimization of the mode

Paper Citation

in Harvard Style

Pan Y. (2025). Deep Learning-Based Medical Image Segmentation for Brain Tumors. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 340-347. DOI: 10.5220/0013689700004670

in Bibtex Style

@conference{icdse25,
author={Yudian Pan},
title={Deep Learning-Based Medical Image Segmentation for Brain Tumors},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={340-347},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013689700004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Deep Learning-Based Medical Image Segmentation for Brain Tumors
SN - 978-989-758-765-8
AU - Pan Y.
PY - 2025
SP - 340
EP - 347
DO - 10.5220/0013689700004670
PB - SciTePress