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O-Net: a Brain Tumor segmentation architecture based on U-Net using alternated Pooling

Year: 2025

Type: conference-paper

Abstract: Deep Learning (DL) offers promising tools for improving diagnostic processes in healthcare.Automated brain tumor segmentation using multi-parametric multimodal Magnetic Resonance Imaging (mpMRI) plays a vital role in the clinical management of brain tumor patients, enabling precise delineation of tumor regions.In this paper, we present O-Net, a deep learning model inspired by the U-Net architecture.O-Net employs an ensemble of two mirrored U-Nets with alternating pooling strategies -Max and Average Pooling-to enhance feature extraction.Our approach demonstrates the potential to improve segmentation accuracy using the BraTS 2021 training dataset and highlights the advantages of combining complementary pooling strategies for this task.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-168

Authors: Omar EL BARRAJ, Aya HAGE CHEHADE, Jean-Marie MARION, Mohamad Oueidat, Pierre CHAUVET, Nassib ABDALLAH

Institutions: Université d'Angers, Lebanese University, Inserm, Université de Bretagne Occidentale

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: bronze

OpenAlex ID: W4409480630

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