Improving detection accuracy of heterogeneity in biological tissues through the combination of modulation-demodulation frame accumulation techniques and enhanced vgg16 Article Swipe
Liu Fulong
,
Huang, Siyuan
,
Gao Jie
,
Zhou Xin
,
Wang Junqi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0329009
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0329009
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 on multi-spectral transmission images (MTI) through the combination of modulation-demodulation frame accumulation (M_D-FA) techniques and enhanced Visual Geometry Group 16 (VGG16) models. Firstly, experiments are designed to collect MTI of phantoms. Then, the image is preprocessed by different combinations of frame accumulation (FA) and modulation and demodulation (M_D) techniques. Finally, multi-spectral fusion pseudo-color images obtained from U-Net semantic segmentation are inputted into the original and enhanced VGG16 network models for heterogeneous classification. The experimental results show that: While both FA and M_D significantly improved the image quality individually, their combination (M_D-FA) proved superior, yielding the highest signal-to-noise ratio (SNR) and the most accurate heterogeneous classification. Compared to the original VGG16 model, the enhanced VGG16 models gradually improved the classification accuracy. Most importantly, the 3.5 Hz M_D-FA images processed by the Visual Geometry Group 16-Batch Normalization-Squeeze and Excitation-Global Average Pooling (VGG16_BN_SE_GAP) model achieved the highest classification accuracy of 97.57%, significantly outperforming results using FA or M_D alone. In summary, this paper utilizes different combinations of FA and M_D techniques to further improve the accuracy of deep learning networks on multi-spectral images heterogeneous classification, which promotes the clinical application of multi-spectral transmission imaging technology in early breast cancer detection.
Related Topics
Concepts
Artificial intelligence
Computer science
Pattern recognition (psychology)
Segmentation
Frame (networking)
Pooling
Image quality
Computer vision
Transmission (telecommunications)
Modulation (music)
Image segmentation
Deep learning
Process (computing)
Image processing
Demodulation
Speckle pattern
Human breast
Autofluorescence
Biological system
Convolutional neural network
Fusion
Feature extraction
Support vector machine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0329009
- OA Status
- gold
- References
- 19
- OpenAlex ID
- https://openalex.org/W7105613722
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7105613722Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0329009Digital Object Identifier
- Title
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Improving detection accuracy of heterogeneity in biological tissues through the combination of modulation-demodulation frame accumulation techniques and enhanced vgg16Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-13Full publication date if available
- Authors
-
Liu Fulong, Huang, Siyuan, Gao Jie, Zhou Xin, Wang JunqiList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0329009Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1371/journal.pone.0329009Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Pattern recognition (psychology), Segmentation, Frame (networking), Pooling, Image quality, Computer vision, Transmission (telecommunications), Modulation (music), Image segmentation, Deep learning, Process (computing), Image processing, Demodulation, Speckle pattern, Human breast, Autofluorescence, Biological system, Convolutional neural network, Fusion, Feature extraction, Support vector machineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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19Number of works referenced by this work
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