Deep Models for Stroke Segmentation: Do Complex Architectures Always Perform Better?

Deep Models for Stroke Segmentation: Do Complex Architectures Always Perform Better?

The accurate segmentation of stroke lesions is crucial for the diagnosis and treatment of stroke patients, as it provides spatial information about affected brain regions and the extent of damage. While conventional manual techniques are time-consuming and prone to errors, advanced deep learning mod...

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Bibliographic Details
Main Authors: Ahmed Soliman, Yalda Zafari-Ghadim, Yousif Yousif, Ahmed Ibrahim, Amr Mohamed, Essam A. Rashed, Mohamed A. Mabrok
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Convolutional neural networks
deep learning
nnU-Net
stroke segmentation
vision Transformer
Online Access:https://ieeexplore.ieee.org/document/10813166/
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https://ieeexplore.ieee.org/document/10813166/

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