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<div><p>This study aims at the limitations of traditional methods in the evaluation of stroke sequelae and rehabilitation effect monitoring, especially for the accurate identification and tracking of brain injury areas. To overcome these challenges, we introduce an advanced neuroimaging...

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Bibliographic Details
Main Author:	Xueliang Guo (4797057) (author)
Other Authors:	Lin Sun (105539) (author)
Published:	2025
Subjects:	Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified receiver operating characteristic multimodal mri scans employing skip connections cbam attention module autonomously delineate areas adam optimization algorithm 3d spatial characteristics novel machine learning analyze tumor progression rec ), precision models &# 8217 brain injury areas model &# 8217 local receptive field rehabilitation effect monitoring model effectively integrates brain tumor distributions stroke injury area 3d cnn encoder segmentation accuracy comparable ml models indicated rehabilitation effect brain tumor stroke sequelae effectively identify deep learning cnn encoder tumor core enhancing tumor segmentation maps rehabilitation process manual segmentation ml models miou ), knn ), adaboost ), unet model model make xlink "> traditional methods thereby increasing testing phases subtle changes study introduces study aims statistical metrics shift mechanism scientific basis sampling features results showed resolution features refine features random forest processing capabilities particularly suitable neuroimaging technique net architecture nearest neighbor kappa coefficient jump connection including recall hausdorff distance handle long finely capture existing research et ). efficient tool distance dependencies computational efficiency comprehensive array clinical settings bra2020 dataset auc ). annotation maps also provided
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