CN111382294A - Traditional Chinese medicine auxiliary judgment method based on artificial intelligence image recognition - Google Patents
Traditional Chinese medicine auxiliary judgment method based on artificial intelligence image recognition Download PDFInfo
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- CN111382294A CN111382294A CN201811513221.3A CN201811513221A CN111382294A CN 111382294 A CN111382294 A CN 111382294A CN 201811513221 A CN201811513221 A CN 201811513221A CN 111382294 A CN111382294 A CN 111382294A
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Abstract
The invention provides a traditional Chinese medicine auxiliary judgment method based on artificial intelligence image recognition, which relates to the field of artificial intelligence and comprises the following steps: step 1: acquiring image data and symptom description text data through a mobile terminal, and sending the acquired data to a background system; step 2: the background system respectively carries out feature labeling and semantic analysis on the image data and the symptom description text data, and carries out similarity judgment on the feature labeling data; and step 3: and obtaining a final judgment result based on the semantic analysis data and the similarity judgment result, and sending the final judgment result to the mobile terminal. The invention can intelligently and rapidly classify the images, provide an enough basic database for the Chinese medicine to look and feel, and simultaneously correlate the images with the judgment conclusion to provide auxiliary data for the diagnosis of the Chinese medicine.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a traditional Chinese medicine auxiliary judgment method based on artificial intelligence image recognition.
Background
In the medical industry, some traditional Chinese medicine detection equipment carries out auxiliary judgment through sensors and corresponding data. However, the current medical detection device performs auxiliary judgment through a sensor and corresponding data at a low speed, and cannot achieve intelligent data adjustment.
Currently, as shown in fig. 1, for the auxiliary judgment of the traditional Chinese medicine image, facial image data and tongue image data are basically acquired, and relevant features are obtained through the regionality of image pixels, and finally, the features are compared with a pixel threshold of a database to obtain an auxiliary judgment result. However, the auxiliary judgment is not intelligent enough, and the obtained judgment result is not humanized enough. The traditional Chinese medicine has the characteristics that many boundaries are not necessarily clear, and the judgment needs to be carried out by comprehensive thinking similar to people, so that a large amount of data is needed for supporting, the observation and the study can be carried out in an infinite approaching mode through artificial intelligence data analysis, the classification is carried out by combining corresponding cases through artificial intelligence recognition of images, and according to the observation and study mode of the traditional Chinese medicine, a doctor is provided with an assistant during diagnosis, and information of one hand is provided for training or auxiliary judgment.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide a method for assisting a traditional Chinese medicine judgment based on artificial intelligence image recognition, which can intelligently and quickly classify images, provide an adequate basic database for the listening and listening of the traditional Chinese medicine, and associate the images with a judgment conclusion, thereby providing auxiliary data for the diagnosis of the traditional Chinese medicine.
The invention provides a traditional Chinese medicine auxiliary judgment method based on artificial intelligence image recognition, which comprises the following steps:
step 1: acquiring image data and symptom description text data through a mobile terminal, and sending the acquired data to a background system;
step 2: the background system respectively carries out feature labeling and semantic analysis on the image data and the symptom description text data, and carries out similarity judgment on the feature labeling data;
and step 3: and obtaining a final judgment result based on the semantic analysis data and the similarity judgment result, and sending the final judgment result to the mobile terminal.
Furthermore, the mobile terminal acquires facial image data and tongue image data through a WeChat applet or an APP, and inputs related symptom description text data.
Further, the background system comprises a semantic analysis module, an algorithm library and databases, wherein the databases comprise a face database and a Chinese medicine database.
Further, the step 2 comprises the following specific steps:
step 2.1: inputting symptom description text data into a semantic analysis module for semantic analysis;
step 2.2: inputting the facial image data and the tongue image data into an algorithm library, and carrying out feature labeling through a TensorFlow model built by the algorithm library;
step 2.3: and comparing the characteristic labeling data with the data of the traditional Chinese medicine library to obtain similarity ranking.
Further, the step 3 comprises the following specific steps:
step 3.1: judging and sequencing according to the similarity, and taking the information in the Chinese medicine library corresponding to the features with the highest similarity as a judgment result;
step 3.2: and combining the semantic analysis data with the judgment result to obtain a final judgment result, and sending the final judgment result to the mobile terminal. Furthermore, the TensorFlow model is built based on an artificial intelligence image recognition technology and comprises general image analysis, fine-grained image recognition and photo album classification.
As described above, the method for assisting in determining traditional Chinese medicine based on artificial intelligence image recognition of the present invention has the following beneficial effects: the invention can intelligently and rapidly classify the images, provide an enough basic database for the Chinese medicine to look and feel, and simultaneously correlate the images with the judgment conclusion to provide auxiliary data for the diagnosis of the Chinese medicine.
Drawings
FIG. 1 is a flow chart of a method for determining Chinese medical image data disclosed in the prior art;
fig. 2 is a flowchart of a method for determining chinese medical image data according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
As shown in fig. 2, the present invention provides a method for assisting in determining a chinese medical science based on artificial intelligence image recognition, the method comprising the steps of:
step 1: acquiring image data and symptom description text data through a mobile terminal, and sending the acquired data to a background system;
through setting up the equipment of polishing, carry out face identification through the face identification module of removing the end through little letter applet or APP, can transfer personnel's historical information out fast through the face database, improve backstage system's whole operating efficiency to through removing the end through little letter applet or APP, carry out facial image data and tongue image data collection, and input relevant symptom description text data.
Step 2: the background system respectively carries out feature labeling and semantic analysis on the image data and the symptom description text data, and carries out similarity judgment on the feature labeling data;
firstly, inputting symptom description text data into a semantic analysis module for semantic analysis, then inputting face image data and tongue image data into an algorithm library, and carrying out feature labeling through a TensorFlow model built by the algorithm library; and finally, comparing the characteristic labeling data with the data of the traditional Chinese medicine library to obtain similarity ranking.
And the characteristic algorithm learning of the tongue spirit, the tongue color, the tongue appearance and the tongue texture is realized through a TensorFlow model built by an algorithm library.
And step 3: and obtaining a final judgment result based on the semantic analysis data and the similarity judgment result, and sending the final judgment result to the mobile terminal.
Firstly, judging sorting according to the similarity to obtain a judgment result; and then combining the semantic analysis data with the judgment result to obtain a final judgment result, and sending the final judgment result to the mobile terminal.
The background system comprises a semantic analysis module, an algorithm library and a database, wherein the database comprises a face database and a traditional Chinese medicine database; the TensorFlow model is built based on an artificial intelligence image recognition technology and comprises three modes, algorithms of the three modes exist simultaneously, and the algorithms respectively comprise general image analysis, fine-grained image recognition and photo album classification, so that comparison of data in the later period is facilitated.
The algorithm library continuously reaches the required judgment conclusion along with the continuous expansion of the characteristics and the judgment result and the continuous adjustment along with time.
The general image analysis is based on large-scale image training, supports outputting rich general objects, scene labels and image main body position detection, namely accurately identifies comprehensive information such as object types, positions, confidence degrees and the like in pictures based on deep learning and large-scale image training; the fine-grained image recognition supports multiple fine-grained image recognition, the analysis precision is higher compared with a general image, and the returned information is richer. The photo album classification is based on an image classification technology and a neural network compression technology, automatic image sorting is achieved, and deployment at a mobile terminal is supported.
The algorithm library can adopt an algorithm library similar to a Baidu image to perform circle points of corresponding features, the feature labeling is to apply corresponding labeling templates and images by fixing the positions of tongues, use the closest template application calculation rule, acquire the features of the images according to a corresponding mode and store the features in the algorithm library.
In summary, the invention is configured with the lighting device, the face image and tongue image data are acquired through the WeChat applet and the APP, the related symptom description text is simultaneously input, the image data and the symptom description text are uploaded to the background system, after the artificial intelligent image annotation modeling identification of the background system, the corresponding judgment result is transmitted to the terminal device based on the semantization analysis symptom information, and the purpose of rapid auxiliary judgment is achieved. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (6)
1. A traditional Chinese medicine auxiliary judgment method based on artificial intelligence image recognition is characterized by comprising the following steps:
step 1: acquiring image data and symptom description text data through a mobile terminal, and sending the acquired data to a background system;
step 2: the background system respectively carries out feature labeling and semantic analysis on the image data and the symptom description text data, and carries out similarity judgment on the feature labeling data;
and step 3: and obtaining a final judgment result based on the semantic analysis data and the similarity judgment, and sending the final judgment result to the mobile terminal.
2. The method of claim 1, wherein the mobile terminal collects facial image data and tongue image data through WeChat applet or APP and inputs related symptom description text data.
3. The method for assisting in judging traditional Chinese medicine based on artificial intelligence image recognition according to claim 1, wherein the background system comprises a semantic analysis module, an algorithm library and databases, and the databases comprise a face database and a traditional Chinese medicine database.
4. The artificial intelligence image recognition-based traditional Chinese medicine auxiliary judgment method according to claim 3, wherein the step 2 specifically comprises the following steps:
step 2.1: inputting symptom description text data into a semantic analysis module for semantic analysis;
step 2.2: inputting the facial image data and the tongue image data into an algorithm library, and carrying out feature labeling through a TensorFlow model built by the algorithm library;
step 2.3: and comparing the characteristic labeling data with the data of the traditional Chinese medicine library to obtain similarity ranking.
5. The artificial intelligence image recognition-based traditional Chinese medicine auxiliary judgment method according to claim 4, wherein the step 3 specifically comprises the following steps:
step 3.1: judging and sequencing according to the similarity, and taking the information in the Chinese medicine library corresponding to the features with the highest similarity as a judgment result;
step 3.2: and combining the semantic analysis data with the judgment result to obtain a final judgment result, and sending the final judgment result to the mobile terminal.
6. The method for acquiring traditional Chinese medicine image data based on artificial intelligence image recognition according to claim 4, wherein the TensorFlow model is constructed based on an artificial intelligence image recognition technology, and comprises general image analysis, fine-grained image recognition and photo album classification.
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| CN116821779A (en) * | 2023-08-31 | 2023-09-29 | 中南大学湘雅医院 | Big data identification method for gastrointestinal health |
| CN117079808A (en) * | 2023-10-16 | 2023-11-17 | 罗麦(北京)营养食品研究有限公司 | Be used for ocular surface periocular image collection and artificial intelligence health analysis system |
| CN112151149B (en) * | 2020-09-18 | 2024-06-07 | 南宁莲现健康科技有限公司 | Face video-based method for assisting intelligent diagnosis of traditional Chinese medicine |
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