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

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

<p dir="ltr">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 error...

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Bibliographic Details
Main Author: Ahmed Soliman (4591621) (author)
Other Authors: Yalda Zafari-Ghadim (22282849) (author), Yousif Yousif (22282852) (author), Ahmed Ibrahim (1771174) (author), Amr Mohamed (3508121) (author), Essam A. Rashed (11949249) (author), Mohamed A. Mabrok (22282855) (author)
Published: 2024
Subjects:
Biomedical and clinical sciences
Neurosciences
Engineering
Biomedical engineering
Health sciences
Health services and systems
Information and computing sciences
Artificial intelligence
Convolutional neural networks
deep learning
nnU-Net
stroke segmentation
vision Transformer
Image segmentation
Transformers
Computer architecture
Stroke (medical condition)
Feature extraction
Data models
Computer vision
Decoding
Accuracy
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