Six types of heterogeneous pseudo-color images after U-Net network segmentation.

Six types of heterogeneous pseudo-color images after U-Net network segmentation.

<p><b>(a)</b> Pseudo-color image of heterogeneity1; <b>(b)</b> Pseudo-color image of heterogeneity2; <b>(c)</b> Pseudo-color image of heterogeneity3; <b>(d)</b> Pseudo-color image of heterogeneity4; <b>(e)</b> Pseudo-color image of he...

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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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