Brain Tumor Segmentation Using 3D ResUNET34-Reference-Cited by-同舟云学术

Brain Tumor Segmentation Using 3D ResUNET34

Published:2022-11-25 Issue: Volume: Page:
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Container-title:2022 International Conference on Futuristic Technologies (INCOFT)
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Author:

Manivannan S.¹,Venkateswaran N.²

Affiliation:

1. Sri Sivasubramaniya Nadar College of Engineering (Autonomous) Old Mahabalipuram Road Kalavakkam,Department of ECE,Chennai,Tamil Nadu,India,603110

2. Sri Sivasubramaniya Nadar College of Engineering (Autonomous) Old Mahabalipuram Road Kalavakkam,Department of BCE,Chennai,Tamil Nadu,India,603110

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094317/10094318/10094432.pdf?arnumber=10094432

Reference15 articles.

1. SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation

2. Analysis of MRI based brain tumor identification using segmentation technique

3. Brain tumor segmentation and survival prediction using automatic hard mining in 3d cnn architecture;anand;International MICCAI Brainlesion Workshop,2020

4. Brain tumour seg- mentation using cascaded 3D densely-connected U-net;ghaffari;arXiv preprint arXiv 2009,0

5. Brain Tumor Segmentation Based on 3D Unet with Multi-Class Focal Loss

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1. Brain Tumor Image Segmentation using Box-Unet Architecture;2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI);2023-10-19

2. DO-UNet:Improved UNet Model for CT Image Segmentation using DBB and Octave Convolution;Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology;2023-08-25

3. WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas;IET Image Processing;2023-06-29