Optimizing Brain Tumor Segmentation Using Attention U-NET and ASPP U-NET

Mohana Saranya S, Sowmiya S, Vinieth S S, Savitha S, Mohanapriya S, Dinesh K

2025

Abstract

This study analyzes the performance evaluation of different deep learning models such as Attention U-Net, and ASPP U-Net for segmentation of brain tumor (BT) in 2D MRI scans. It is an integral part of diagnosis and treatment of tumors in the brain region. The traditional U-Net uses encoder-decoder paths for accurate localization. In this paper, we have done comparison between Attention U-Net and ASPP U-Net. The Attention U-Net enhances performance by an attention mechanism that highlights relevant tumor areas. The ASPP U-Net also improves segmentation using Atrous Spatial Pyramid Pooling (ASPP) to capture multi-scale features. The results of this work also indicate that the Attention U-Net is superior to ASPP U-Net on accuracy and most importantly better improving BT segmentation.

Paper Citation

in Harvard Style

Saranya S M., S S., S S V., S S., S M. and K D. (2025). Optimizing Brain Tumor Segmentation Using Attention U-NET and ASPP U-NET. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 223-228. DOI: 10.5220/0013589800004664

in Bibtex Style

@conference{incoft25,
author={Mohana Saranya S and Sowmiya S and Vinieth S S and Savitha S and Mohanapriya S and Dinesh K},
title={Optimizing Brain Tumor Segmentation Using Attention U-NET and ASPP U-NET},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={223-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013589800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Optimizing Brain Tumor Segmentation Using Attention U-NET and ASPP U-NET
SN - 978-989-758-763-4
AU - Saranya S M.
AU - S S.
AU - S S V.
AU - S S.
AU - S M.
AU - K D.
PY - 2025
SP - 223
EP - 228
DO - 10.5220/0013589800004664
PB - SciTePress