Improving detection accuracy of heterogeneity in biological tissues through the combination of modulation-demodulation frame accumulation techniques and enhanced vgg16
Fulong Liu, Siyuan Huang, Jie Gao, Xin Zhou, Junqi Wang
TL;DR
This paper improves the detection of tissue heterogeneity in multi-spectral images using a combination of modulation-demodulation and frame accumulation techniques with an enhanced VGG16 model.
Contribution
The novel combination of M_D-FA techniques with an enhanced VGG16 model significantly improves classification accuracy for tissue heterogeneity.
Findings
Combining M_D and FA techniques improves image quality and classification accuracy more than either method alone.
The enhanced VGG16_BN_SE_GAP model achieved a classification accuracy of 97.57% on M_D-FA images.
The method promotes clinical use of multi-spectral imaging for early breast cancer detection.
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…
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Taxonomy
TopicsOptical Imaging and Spectroscopy Techniques · Advanced Image Fusion Techniques · Infrared Thermography in Medicine