Bone deformation analysis method based on artificial intelligence

A technology of artificial intelligence and deformation analysis, applied in image analysis, image data processing, image enhancement, etc., can solve problems such as high cost, low efficiency, and poor accuracy of analysis results, so as to reduce labor costs, improve accuracy, and reduce labor costs. The effect of diagnostic costs

Inactive Publication Date: 2019-01-11
极创智能(北京)健康科技有限公司
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Problems solved by technology

When the traditional technical solution analyzes the bone deformation, the accu

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  • Bone deformation analysis method based on artificial intelligence
  • Bone deformation analysis method based on artificial intelligence
  • Bone deformation analysis method based on artificial intelligence

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Embodiment Construction

[0046] The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0047] In the embodiment of the present invention, the data collected from medical institutions, physical examination institutions and colleges and universities are divided into training set, verification set and test set, and divided according to the ratio of 60%, 20%, and 20%. In this way, the model with the highest accuracy and the best generalization ability is obtained.

[0048] Among them, the training set is used to fit the model, and the classification model is trained by setting the parameters of the classifier. When combined with the verification set later, different values ​​of the same parameter will be selected to fit multiple classifiers. The role of the validation set is to use each model to predict the validation set data and record the accuracy of the model in order to find the model with the best effect after trainin...

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Abstract

A bone deformation analysis method based on artificial intelligence divides the material model into a training set, a verification set and a test set according to the given proportion. The pre-training basic model is obtained, and the image is transformed into high-dimensional feature vector by feature extractor, which is used as the input of the pre-training basic model for transfer learning training. The verification set is used for local correlation, weight sharing, multi-core convolution and pooling data dimension reduction of the pre-training basic model. The convolution neural network performs reverse propagation layer by layer from the last layer to adjust the weights, the learned features will be converted into probability results for classification and recognition, and in the convolution neural network of all the connected layers to connect all the features, the output value will be sent to the classifier; the pre-training basic model is applied to the verification set to getthe output of the model, and the performance of the model is tested by inputting the test set data into the trained model with the correct rate. The invention increases the correctness rate of the bone deformation analysis result and reduces the human cost.

Description

technical field [0001] The invention relates to the technical field of graphic processing, in particular to an artificial intelligence-based bone deformation analysis method. Background technique [0002] Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and develop a new intelligent machine that can respond in a manner similar to human intelligence. Research in this field includes robotics, language recognition, image recognition, natural language processing and expert systems, etc. At present, artificial intelligence learning frameworks such as Tensorflow, keras, and caffe have experienced several years of development, and have accumulated deep accumulation and progress in many image recognition fields. However, the combination with the medical field is still in the groping stage. There is no dedicated artificial intelligence technology solution to assist doctors in bone deformation analysis. When the bone is...

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Application Information

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IPC IPC(8): G06T7/00G06T7/60G06N3/04
CPCG06T7/0012G06T7/60G06T2207/10116G06T2207/30008G06N3/045
Inventor 金戈
Owner 极创智能(北京)健康科技有限公司
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