A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

This paper proposes an improved brain magnetic resonance imaging (MRI) segmentation model by integrating U-SegNet with fire modules and residual convolutions to segment brain tissues in MRI. In the proposed encoder-decoder method, the residual connections and squeeze-expand convolutional layers from...

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Bibliographic Details
Main Authors: Chaitra Dayananda, Jae Young Choi, Bumshik Lee
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Brain tissue
residual connection
fire module
magnetic resonance imaging
Online Access:https://ieeexplore.ieee.org/document/9775082/

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