SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans
In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input patches, combined-connections, long skip connections,...
Main Authors: | Nagaraj Yamanakkanavar, Jae Young Choi, Bumshik Lee |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-07-01
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Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/22/14/5148 |
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