A method and device for accurate evaluation of the degree of facial paralysis based on h-b grading under cv
An evaluation method, facial paralysis technology, applied in neural learning methods, acquisition/recognition of facial features, instruments, etc., can solve the problems of large errors and low evaluation efficiency, and achieve the effect of improving accuracy and accuracy, and high detection and positioning accuracy
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Embodiment 1
[0083] see figure 1 , the present embodiment provides a method for evaluating the degree of facial paralysis based on H-B classification under CV, which can be applied to facial paralysis detection equipment as a detection method for medical equipment to detect the degree of facial paralysis of patients with facial paralysis, and can be large-scale, Extensive industrial application, for example, it can be used as an independent program in the mobile terminal and client terminal, which can be used for correction and inspection of patients with facial paralysis during the non-treatment period, and can also be used as a preventive method for patients without facial paralysis. Wherein, the method for evaluating the degree of accurate facial paralysis includes the following steps, namely steps (1)-(3).
[0084] Step (1): Establish a facial paralysis key point detection model. In this embodiment, the method for establishing the facial paralysis key point detection model includes th...
Embodiment 2
[0118] see image 3 , this embodiment provides an accurate evaluation method of facial paralysis based on H-B classification under CV, which is similar to that of Embodiment 1, except that the deep fully convolutional network model of this embodiment is different. The specific structure of the deep full convolutional network model in this embodiment can be designed separately according to the specific requirements of users. For the convenience of further introduction, an example of the structure of a deep full convolutional network model is now designed. image 3shown. The number of downsampling and upsampling layers of the deep full convolutional network model is 3 layers, and the downsampling adopts maxpooling maximum pooling method. The size of the pooling layer is 2×2 and the step size is 2. The upsampling adopts The dconv deconvolution method, the size of the deconvolution layer is 2×2 and the step size is 2. Each adjacent upsampling or downsampling is separated by convo...
Embodiment 3
[0120] This embodiment provides an accurate facial paralysis degree evaluation device based on H-B classification under CV, which applies the accurate facial paralysis evaluation method based on H-B classification under CV in embodiment 1 or embodiment 2. Among them, the precise facial paralysis degree evaluation device includes a detection model building module, a data acquisition module, a data processing module and a facial paralysis comprehensive evaluation module. The data acquisition module and the data processing module can form a data acquisition and processing module to be detected. These modules can be used as computer program modules or hardware modules, which can execute the relevant steps described in Embodiment 1 or Embodiment 2.
[0121] The detection model building module is used to set up a facial paralysis key point detection model, which is actually used to implement step (1) in Embodiment 1. In the facial paralysis key point detection model, define the adja...
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