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Authors: Amal Jlassi 1 ; Khaoula ElBedoui 1 ; 2 and Walid Barhoumi 1 ; 2

Affiliations: 1 Université de Tunis El Manar, Institut Supérieur d’Informatique, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de Recherche en Informatique, Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC), 2 Rue Abou Rayhane Bayrouni, 2080 Ariana, Tunisia ; 2 Université de Carthage, Ecole Nationale d’Ingénieurs de Carthage, 45 Rue des Entrepreneurs, 2035 Tunis-Carthage, Tunisia

Keyword(s): Deep Learning, Brain Segmentation, MRI, LGG, Hybrid Convolutional Neural Networks.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 454-465. DOI: 10.5220/0011895900003393

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Jlassi, A.
AU - ElBedoui, K.
AU - Barhoumi, W.
PY - 2023
SP - 454
EP - 465
DO - 10.5220/0011895900003393
PB - SciTePress

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