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

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.