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

A hyperspectral and classification algorithm technology, which is applied to instruments, character and pattern recognition, computer components, etc., can solve problems such as long training time and decreased accuracy

Pending Publication Date: 2020-06-09
BEIJING UNIV OF TECH
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However, these architectures still face the chall

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

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Embodiment Construction

[0025] The specific implementation method of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0026] 1. Labeling preprocessing

[0027] The reflectance of the original tongue image dataset was calibrated and labeled by ENVI software, and the label image was obtained as the ground truth.

[0028] 2. Build D-Resnet network

[0029] Such as image 3 shown. The specific parameters of each layer of the D-Resnet network constructed by the present invention are as follows:

[0030] ①U1 convolutional layer: the input size is 9×9, the number of input channels is L, the convolution kernel is 1×1×7, the number of channels is 24, the edge filling method is 'valid', the activation function is PReLU, and the output size is 9 ×9×b, the number of output channels is 24.

[0031] ②U2 convolutional layer: the input size is 9×9×b, the number of input channels is 24, the convolution kernel is 1×1×1, the number of channels is 12, the edge...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/34G16H20/90
CPCG16H20/90G06V10/267G06F18/2411
Inventor 蔡轶珩郭雅君刘嘉琦胡绍斌张新峰
Owner BEIJING UNIV OF TECH
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