Table 1_Application research on YOLOv5 model based on Lightweight Atrous Attention Module in brain tumor MRI image segmentation.docx

Table 1_Application research on YOLOv5 model based on Lightweight Atrous Attention Module in brain tumor MRI image segmentation.docx

Objective:<p>To enhance the segmentation accuracy and computational efficiency of brain tumor magnetic resonance imaging (MRI) images, this study proposes a novel Lightweight Atrous Attention Module (LAAM) that integrates the Convolutional Block Attention Module (CBAM) with an Atrous Spatial P...

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Bibliographic Details
Main Author: Tao Yang (184280) (author)
Other Authors: Jinghui Chen (12084704) (author), Lianxin Xie (22399945) (author), Lanlan Yang (1581235) (author), Chengbin Ye (22399948) (author), Hongjia Zhao (18063894) (author)
Published: 2025
Subjects:
Foetal Development and Medicine
artificial intelligence
image segmentation
brain tumor
magnetic resonanceimaging (MRI)
YOLOv5S
Lightweight Atrous Attention Module (LAAM)
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