Hyperspectral traditional Chinese medicine coated tongue quality classification method based on D-Resnet

Abstract

The invention discloses a hyperspectral traditional Chinese medicine coated tongue quality classification method based on D-Resnet, and relates to the field of computer vision. Based on tongue coatingtongue quality classification of an RGB color space, the information amount is insufficient, a hyperspectral tongue image contains a large amount of spectral and spatial information, and the tongue coating tongue quality classification of a human body is realized by extracting the spectral reflectance change condition of a certain area of the tongue image and combining the spatial distribution information provided by the hyperspectral image. According to the method, an end-to-end D-Resnet network is provided to classify hyperspectral tongue images; the method comprises the following steps of:firstly, constructing a dense connection module to extract spectral information; then, a pre-activation bottleneck residual error module (pre-activation bottleneck residual error module) is constructed, so that space information can be extracted, and the spatial information of the pre-activation bottleneck residual error module can be obtained; according to the method, the coated tongue quality classification based on the hyperspectral image is realized.

Description

technical field [0001] The invention belongs to the field of computer vision and relates to a hyperspectral medical image classification method of a D-Resnet network. Background technique [0002] Tongue diagnosis is one of the main contents of the four diagnostic methods of TCM, and it is of great significance for guiding TCM clinical syndrome differentiation and treatment and curative effect evaluation. Quantitative description of tongue coating and texture information is an important part of TCM tongue diagnosis, which directly affects the accuracy of TCM clinical tongue diagnosis. With the development of science and technology, research on tongue coating and texture classification has made significant progress. However, these studies are mainly based on RGB Color space is used to collect and analyze tongue image information, but the amount of information is insufficient. The hyperspectral image can obtain two-dimensional image information and one-dimensional spectral in...

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However, these architectures still face the challenges of decreased accuracy and long training time

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