ArticlePloS one2025
Improving detection accuracy of heterogeneity in biological tissues through the combination of modulation-demodulation frame accumulation techniques and enhanced vgg16.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Classification of acceleration and deceleration craniocerebral injuries by using cascaded deep learning models.International journal of legal medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.