Brain Tumor Segmentation of Lower-Grade Glioma Across MRI Images Using Hybrid Convolutional Neural Networks

Amal Jlassi, Khaoula ElBedoui, Khaoula ElBedoui, Walid Barhoumi, Walid Barhoumi

2023

Abstract

Low-Grade Gliomas (LGG) are the most common malignant brain tumors that greatly define the rate of survival of patients. LGG segmentation across Magnetic Resonance Imaging (MRI) is common and necessary for diagnosis and treatment planning. To achieve this challenging clinical need, a deep learning approach that combines Convolutional Neural Networks (CNN) based on the hybridization of U-Net and SegNet is developed in this study. In fact, an adopted SegNet model was established in order to compare it with the most used model U-Net. The segmentation uses FLuid Attenuated Inversion Recovery (FLAIR) of 110 patients of LGG for training and evaluations. The highest mean and median Dice Coefficient (DC) achieved by the hybrid model is 83% and 85:7%, respectively. The obtained results of this work lead to the potential of using deep learning in MRI images in order to provide a non-invasive tool for automated LGG segmentation for many relevant clinical applications.

Paper Citation

in Harvard Style

Jlassi A., ElBedoui K. and Barhoumi W. (2023). Brain Tumor Segmentation of Lower-Grade Glioma Across MRI Images Using Hybrid Convolutional Neural Networks. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-623-1, pages 454-465. DOI: 10.5220/0011895900003393

in Bibtex Style

@conference{icaart23,
author={Amal Jlassi and Khaoula ElBedoui and Walid Barhoumi},
title={Brain Tumor Segmentation of Lower-Grade Glioma Across MRI Images Using Hybrid Convolutional Neural Networks},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2023},
pages={454-465},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011895900003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Brain Tumor Segmentation of Lower-Grade Glioma Across MRI Images Using Hybrid Convolutional Neural Networks
SN - 978-989-758-623-1
AU - Jlassi A.
AU - ElBedoui K.
AU - Barhoumi W.
PY - 2023
SP - 454
EP - 465
DO - 10.5220/0011895900003393