3D Brain Tumor Segmentation Using Modified U-Net Architecture-Reference-Cited by-同舟云学术

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3D Brain Tumor Segmentation Using Modified U-Net Architecture

Published:2023-10-03 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Cyberworlds (CW)
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Author:

Gammoudi Islem¹,Ghozi Raja²,Mahjoub Mohamed Ali³

Affiliation:

1. Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Université de Tunis El Manar,Faculté des Sciences Mathématiques Physiques et Naturelles de Tunis,Tunis,Tunisie,2092

2. American University of Bahrain College of Engineering,Riffa,Bahrain,P.O. Box 38884

3. Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse,LATIS-Laboratory of Advanced Technology and Intelligent Systems,Sousse,Tunisie,4023

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10336928/10337517/10337527.pdf?arnumber=10337527

Reference27 articles.

1. Segmentation labels and radiomic features for the preoperative scans of the TCGA-GBM collection;Bakas;Cancer Imaging Arch. Nat. Sci. Data,2017

2. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

3. Fully convolutional networks for semantic segmentation

4. U-Net: Convolutional Networks for Biomedical Image Segmentation

5. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

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