U-Net architecture model.

<div><p>Light source has obvious absorption and scattering effects during the transmission process of biological tissues, making it difficult to identify heterogeneities in multi-spectral images. This paper achieves a gradual improvement in the classification accuracy of heterogeneities...

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Bibliographic Details
Main Author:	Fulong Liu (22614787) (author)
Other Authors:	Siyuan Huang (5470118) (author), Jie Gao (10266) (author), Xin Zhou (54275) (author), Junqi Wang (728963) (author)
Published:	2025
Subjects:	Medicine Biotechnology Cancer Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified net semantic segmentation global average pooling experimental results show deep learning networks color images obtained spectral fusion pseudo improving detection accuracy m_d significantly improved 5 hz m_d image quality individually accurate heterogeneous classification spectral transmission images fa images processed highest classification accuracy demodulation frame accumulation original vgg16 model spectral images heterogeneous classification frame accumulation classification accuracy transmission process model achieved highest signal m_d alone scattering effects proved superior paper achieves obvious absorption noise ratio gradual improvement different combinations clinical application biological tissues batch normalization
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U-Net architecture model.

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