Leveraging U-Net Architecture for Accurate Localization in Brain Tumor Segmentation-Reference-Cited by-同舟云学术

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Leveraging U-Net Architecture for Accurate Localization in Brain Tumor Segmentation

Published:2023-10-18 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 IEEE 9th Information Technology International Seminar (ITIS)
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Author:

Poo Jeckey Ng Kah¹,Saealal Muhammad Salihin²,Ibrahim Mohd Zamri¹,Yakno Marlina¹

Affiliation:

1. Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah,Pekan,Pahang,Malaysia

2. Faculty of Electrical Technology and Engineering, University Teknikal Malaysia Melaka, Durian Tunggal,Melaka,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10419870/10419872/10419915.pdf?arnumber=10419915

Reference15 articles.

1. Automatic segmentation of multimodal brain tumor images based on classification of super-voxels

2. Astrocytoma, ependymoma, and oligodendroglioma classification with deep convolutional neural network

3. Multimodal MRI Brain Tumor Segmentation—A ResNet-based U-Net approach

4. Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

5. Brain Tumor Segmentation Using 2D-UNET Convolutional Neural Network

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