CN117159369A - Finger pressure hemostasis point positioning method and device - Google Patents
Finger pressure hemostasis point positioning method and device Download PDFInfo
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- CN117159369A CN117159369A CN202311114667.XA CN202311114667A CN117159369A CN 117159369 A CN117159369 A CN 117159369A CN 202311114667 A CN202311114667 A CN 202311114667A CN 117159369 A CN117159369 A CN 117159369A
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- 230000023597 hemostasis Effects 0.000 title claims abstract description 122
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000001514 detection method Methods 0.000 claims abstract description 108
- 238000007906 compression Methods 0.000 claims abstract description 82
- 230000006835 compression Effects 0.000 claims abstract description 75
- 238000012545 processing Methods 0.000 claims abstract description 16
- 238000012549 training Methods 0.000 claims description 80
- 238000012795 verification Methods 0.000 claims description 38
- 238000012797 qualification Methods 0.000 claims description 11
- 230000006870 function Effects 0.000 claims description 7
- 238000002372 labelling Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims 3
- 238000010200 validation analysis Methods 0.000 claims 1
- 208000032843 Hemorrhage Diseases 0.000 description 5
- 230000000740 bleeding effect Effects 0.000 description 4
- 206010060964 Arterial haemorrhage Diseases 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 208000012260 Accidental injury Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 208000034158 bleeding Diseases 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000002439 hemostatic effect Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002792 vascular Effects 0.000 description 1
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Abstract
The application relates to the technical field of data processing, in particular to a finger pressure hemostasis point positioning method and device, wherein the method comprises the steps of firstly obtaining a detection sample input by a user, inputting the detection sample into a pre-established finger pressure hemostasis compression point detection model, and marking a finger pressure hemostasis point in the detection sample through the finger pressure hemostasis compression point detection model. According to the application, the finger pressure hemostasis compression point detection model is pre-established to directly mark the finger pressure hemostasis points on the detection sample provided by the user, so that the problem of inaccurate positioning of the finger pressure hemostasis points by text description at present is solved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a finger pressure hemostasis point positioning method and device.
Background
In daily life frequently suffered from natural disasters and accidental injuries, bleeding is often caused by human body injury, and serious bleeding can cause life danger due to insufficient medical resources and medical facilities in various burst scenes. Among the various vascular hemorrhages, arterial hemorrhages are at a greater risk. The red blood is sprayed during arterial hemorrhage and is ejected outwards in array along with the heartbeat. At this time, the finger pressure hemostasis method (pressing the bleeding artery with the finger) is adopted. As an emergency measure, bleeding may be temporarily stopped.
However, most users generally do not have the basic common sense of acupressure hemostasis, the accurate positions of acupressure hemostasis points are not easy to confirm, and the positioning method of the acupressure hemostasis points in the market only stays in the text description and is inaccurate in positioning.
Disclosure of Invention
Therefore, the application aims to provide a finger pressure hemostasis point positioning method and device, so as to solve the problem that the finger pressure hemostasis point positioning is inaccurate by text description at present.
In order to achieve the above purpose, the application adopts the following technical scheme:
in one aspect, the application provides a finger pressure hemostasis point positioning method, comprising the following steps:
acquiring a detection sample input by a user;
inputting the detection sample into a pre-established finger pressure hemostasis compression point detection model;
marking the acupressure hemostasis points in the detection sample through the acupressure hemostasis compression point detection model.
Further, the method above, before the obtaining the detection sample input by the user, further includes:
establishing the finger pressure hemostasis compression point detection model based on a YOLO algorithm;
acquiring a training image, and processing the training image to acquire a verification image; wherein the training image comprises: a human body part image;
training the finger pressure hemostasis compression point detection model according to the training image, and verifying a training result according to the verification image;
judging whether the verification is passed or not, if the verification is not passed, training the finger pressure hemostasis compression point detection model again.
Further, in the method described above, the creating the finger-pressure hemostasis-compression-point detection model based on the YOLO algorithm includes:
and according to different human body parts, establishing a plurality of finger pressure hemostasis compression point detection models based on a YOLO algorithm.
Further, in the above method, obtaining a training image, and processing the training image to obtain a verification image includes:
classifying the training images according to the human body parts;
and processing the classified training images to obtain verification images.
Further, in the method described above, the processing the classified training image to obtain a verification image includes:
labeling finger-pressure hemostasis compression points in the classified training images through labelme labeling software to obtain verification images.
Further, in the method, training the acupressure hemostasis compression point detection model according to the training image, and verifying the training result according to the verification image, including:
classifying the verification images according to the human body parts, converting the verification images into data conforming to the YOLO algorithm,
inputting the data and the training image of the same human body part into a single finger-pressure hemostasis compression point detection model for training for preset times, so as to train the finger-pressure hemostasis compression point detection models of different human body parts;
and obtaining training results output by the finger pressure hemostasis compression point detection models of different human body parts, and calculating the marking accuracy of the training results according to the verification images and the loss function.
Further, in the above method, the determining whether the verification passes or not, if not, training the finger pressure hemostasis compression point detection model again, including:
judging whether the finger pressure hemostasis compression point detection model of different human body parts is qualified in training according to a preset qualification accuracy threshold;
if the marking accuracy of the finger pressure hemostasis compression point detection model of the current human body part reaches the qualification accuracy threshold, training the finger pressure hemostasis compression point detection model of the current human body part to be qualified;
if the marking accuracy of the finger pressure hemostasis compression point detection model of the current human body part does not reach the qualification accuracy threshold, training the finger pressure hemostasis compression point detection model of the current human body part again.
Further, in the method, the inputting the detection sample into a pre-created acupressure hemostasis compression point detection model includes:
acquiring a user instruction, and determining the finger pressure hemostasis compression point detection model corresponding to the human body part according to the user instruction;
and inputting the detection sample into the finger pressure hemostasis compression point detection model.
Further, in the method described above, the detecting sample at least includes: picture, video, and real-time video streaming.
In another aspect, the present application further provides a finger-pressure hemostasis point positioning device, including a processor and a memory, where the processor is connected with the memory:
the processor is used for calling and executing the program stored in the memory;
the memory is used for storing the program, and the program is at least used for executing the finger pressure hemostasis point positioning method.
The beneficial effects of the application are as follows:
firstly, a detection sample input by a user is acquired, the detection sample is input into a pre-established finger pressure hemostasis compression point detection model, and finger pressure hemostasis points are marked in the detection sample through the finger pressure hemostasis compression point detection model. According to the application, the finger pressure hemostasis compression point detection model is pre-established to directly mark the finger pressure hemostasis points on the detection sample provided by the user, so that the problem of inaccurate positioning of the finger pressure hemostasis points by text description at present is solved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of one embodiment of a method for positioning a acupressure hemostasis point of the present application;
fig. 2 is a schematic diagram of a finger pressure hemostasis positioning device according to one embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, based on the examples herein, which are within the scope of the application as defined by the claims, will be within the scope of the application as defined by the claims.
Most users generally do not have the common general knowledge of acupressure hemostasis, the accurate positions of acupressure hemostasis points are not easy to confirm, and the positioning method of the acupressure hemostasis points in the market only stays in the text description and is inaccurate in positioning.
Therefore, the application aims to provide a finger pressure hemostasis point positioning method and device, so as to solve the problem that the finger pressure hemostasis point positioning is inaccurate by text description at present.
FIG. 1 is a flow chart of one embodiment of a finger pressure hemostasis point positioning method in accordance with the present application. Referring to fig. 1, the present embodiment may include the following steps:
s1, acquiring a detection sample input by a user.
S2, inputting the detection sample into a pre-established finger pressure hemostasis compression point detection model.
And S3, marking the acupressure hemostasis points in the detection sample through an acupressure hemostasis compression point detection model.
It can be understood that the present application firstly obtains a detection sample input by a user, inputs the detection sample into a pre-created finger pressure hemostasis compression point detection model, and marks a finger pressure hemostasis point in the detection sample through the finger pressure hemostasis compression point detection model. In the application, the finger pressure hemostasis compression point detection model is established in advance to directly mark the finger pressure hemostasis point on the detection sample provided by the user, and compared with the method that the user determines the finger pressure hemostasis point through the self basic knowledge and the text description, the finger pressure hemostasis point is positioned more accurately, thereby solving the problem that the finger pressure hemostasis point is positioned inaccurately through the text description at present.
Preferably, before step S1, the method further includes:
establishing a finger pressure hemostasis compression point detection model based on a YOLO algorithm;
acquiring a training image, and processing the training image to acquire a verification image; wherein, training image includes: a human body part image;
training a finger pressure hemostasis compression point detection model according to the training image, and verifying a training result according to the verification image;
judging whether the verification is passed or not, if the verification is not passed, training the finger pressure hemostasis compression point detection model again.
It can be understood that the YOLO algorithm is a brand-new end-to-end target detection algorithm implemented by integrating the four stages of generating a target candidate region, extracting target features from a base network, merging shallow and deep feature information from an enhanced feature extraction network, and verifying target candidates by using a neural network. The finger pressure hemostasis and compression point detection model created through the YOLO algorithm is used for positioning the finger pressure hemostasis and compression point in the image, 45 pictures can be identified per second by the YOLO neural network, and the positioning speed and the positioning accuracy are high.
Preferably, the method for establishing the finger pressure hemostasis compression point detection model based on the YOLO algorithm comprises the following steps:
according to different human body parts, a plurality of finger pressure hemostasis compression point detection models are established based on a YOLO algorithm.
It can be understood that in order to ensure more accurate positioning, a plurality of finger pressure hemostasis compression point detection models are established according to different parts of a human body.
Preferably, acquiring a training image, and processing the training image to obtain a verification image includes:
classifying the training images according to the human body parts;
and processing the classified training images to obtain verification images.
Preferably, processing the classified training image to obtain a verification image includes:
labeling finger compression hemostasis compression points in the classified training images through labelme labeling software to obtain verification images.
Preferably, training the finger pressure hemostasis compression point detection model according to the training image, and verifying the training result according to the verification image, including:
classifying the verification images according to the human body parts, converting the verification images into data conforming to the YOLO algorithm,
inputting the data and training images of the same human body part into a single finger pressure hemostasis compression point detection model for training for preset times, so as to train the finger pressure hemostasis compression point detection models of different human body parts;
and obtaining training results output by the finger pressure hemostasis compression point detection models of different human body parts, and calculating the marking accuracy of the training results according to the verification images and the loss function.
It can be understood that, when training all the created finger pressure hemostasis-compression point detection models, in order to ensure that the finger pressure hemostasis-compression point detection models of different parts of the human body are accurately trained, data and training images of the same human body part are input into a single finger pressure hemostasis-compression point detection model for training for preset times.
The specific training process is as follows:
firstly, acquiring a plurality of human body part images, carrying out gray processing on the human body part images to obtain gray processed images, and establishing a human body part sample database according to the gray processed images; and then, marking a rectangular frame on the gray-processed human body part image and marking the finger-pressing hemostasis-compression points by using labelme marking software to obtain coordinates of a plurality of marking frame center points and coordinates of a plurality of finger-pressing hemostasis-compression points, converting the human body part image data after the finger-pressing hemostasis-compression points are marked by the labelme marking software into data conforming to the YOLO algorithm format, taking training images in a sample database and the converted YOLO algorithm data as training input and training for preset times, and obtaining the accuracy of identification detection according to the human body part image and loss function after the finger-pressing hemostasis-compression points are marked.
Preferably, judging whether the verification passes or not, if not, training the finger pressure hemostasis compression point detection model again, wherein the method comprises the following steps:
judging whether the finger pressure hemostasis compression point detection model of different human body parts is qualified in training according to a preset qualification accuracy threshold;
if the marking accuracy of the finger pressure hemostasis compression point detection model of the current human body part reaches a qualified accuracy threshold, training the finger pressure hemostasis compression point detection model of the current human body part to be qualified;
if the marking accuracy of the finger pressure hemostasis compression point detection model of the current human body part does not reach the qualification accuracy threshold, training the finger pressure hemostasis compression point detection model of the current human body part again.
It can be understood that, in order to ensure the positioning accuracy of the finger pressure hemostasis compression point detection model, after model training is completed, whether model training is qualified or not is judged according to a preset qualification accuracy threshold, and if the qualification accuracy threshold is not reached, the model is trained again until the qualification accuracy threshold is reached.
Preferably, step S2 includes:
acquiring a user instruction, and determining a finger pressure hemostasis compression point detection model corresponding to the human body part according to the user instruction;
and inputting the detection sample into a finger pressure hemostasis compression point detection model.
Preferably, the detection sample comprises at least: picture, video, and real-time video streaming.
In specific practice, a detection sample, including an image, a video file, and an online video, is obtained that is input by a user at a user terminal. And transmitting the detection sample to the server terminal; the server terminal analyzes key points of human bodies in the images by judging the images and video image frames in the detection samples, positions finger-pressing hemostasis points in the images, marks the finger-pressing hemostasis points by the points with colors, and returns the images with the marked points or video synthesized by the image frames to the user terminal; the recognition detection model is generated based on the key point detection of the YOLO algorithm, and is used for positioning the finger-pressure hemostasis points and marking the finger-pressure hemostasis points on the detection sample uploaded by the user terminal, so that the recognition speed and the working efficiency can be effectively improved, and the effects of accurate finger-pressure hemostasis point positioning and low error are achieved.
The application also provides finger-pressure hemostatic point positioning equipment for realizing the method embodiment. Fig. 2 is a schematic diagram of a finger pressure hemostasis positioning device according to one embodiment of the application. As shown in fig. 2, the acupressure hemostasis point positioning device of the present embodiment includes a processor 21 and a memory 22, the processor 21 being connected to the memory 22. Wherein the processor 21 is used for calling and executing the program stored in the memory 22; the memory 22 is used to store the program at least for performing the acupressure hemostasis point positioning method in the above embodiments.
The specific implementation manner of the finger pressure hemostasis point positioning device provided by the embodiment of the present application may refer to the implementation manner of the finger pressure hemostasis point positioning method in any embodiment, and will not be described herein.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. A finger pressure hemostasis point positioning method, characterized by comprising:
acquiring a detection sample input by a user;
inputting the detection sample into a pre-established finger pressure hemostasis compression point detection model;
marking the acupressure hemostasis points in the detection sample through the acupressure hemostasis compression point detection model.
2. The method of claim 1, further comprising, prior to said obtaining the user-entered test sample:
establishing the finger pressure hemostasis compression point detection model based on a YOLO algorithm;
acquiring a training image, and processing the training image to acquire a verification image; wherein the training image comprises: a human body part image;
training the finger pressure hemostasis compression point detection model according to the training image, and verifying a training result according to the verification image;
judging whether the verification is passed or not, if the verification is not passed, training the finger pressure hemostasis compression point detection model again.
3. The method of claim 2, wherein the creating the acupressure hemostasis compression point detection model based on the YOLO algorithm includes:
and according to different human body parts, establishing a plurality of finger pressure hemostasis compression point detection models based on a YOLO algorithm.
4. A method according to claim 3, wherein obtaining a training image, processing the training image to obtain a verification image, comprises:
classifying the training images according to the human body parts;
and processing the classified training images to obtain verification images.
5. The method of claim 4, wherein processing the classified training images to obtain verification images comprises:
labeling finger-pressure hemostasis compression points in the classified training images through labelme labeling software to obtain verification images.
6. A method according to claim 5, wherein said training the acupressure hemostasis compression point detection model based on the training image, and validating the training result based on the validation image, comprises:
classifying the verification images according to the human body parts, converting the verification images into data conforming to the YOLO algorithm,
inputting the data and the training image of the same human body part into a single finger-pressure hemostasis compression point detection model for training for preset times, so as to train the finger-pressure hemostasis compression point detection models of different human body parts;
and obtaining training results output by the finger pressure hemostasis compression point detection models of different human body parts, and calculating the marking accuracy of the training results according to the verification images and the loss function.
7. A method according to claim 6, wherein said determining whether the verification passes, and if not, retraining the acupressure hemostasis compression point detection model comprises:
judging whether the finger pressure hemostasis compression point detection model of different human body parts is qualified in training according to a preset qualification accuracy threshold;
if the marking accuracy of the finger pressure hemostasis compression point detection model of the current human body part reaches the qualification accuracy threshold, training the finger pressure hemostasis compression point detection model of the current human body part to be qualified;
if the marking accuracy of the finger pressure hemostasis compression point detection model of the current human body part does not reach the qualification accuracy threshold, training the finger pressure hemostasis compression point detection model of the current human body part again.
8. A method according to claim 7, wherein said inputting the test sample into a pre-created acupressure hemostasis compression point test model comprises:
acquiring a user instruction, and determining the finger pressure hemostasis compression point detection model corresponding to the human body part according to the user instruction;
and inputting the detection sample into the finger pressure hemostasis compression point detection model.
9. The method of claim 8, wherein the detecting the sample comprises at least: picture, video, and real-time video streaming.
10. The finger pressure hemostasis point positioning device is characterized by comprising a processor and a memory, wherein the processor is connected with the memory:
the processor is used for calling and executing the program stored in the memory;
the memory for storing the program at least for executing the acupressure hemostasis point positioning method of any one of claims 1 to 9.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311114667.XA CN117159369A (en) | 2023-08-31 | 2023-08-31 | Finger pressure hemostasis point positioning method and device |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311114667.XA CN117159369A (en) | 2023-08-31 | 2023-08-31 | Finger pressure hemostasis point positioning method and device |
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| CN117159369A true CN117159369A (en) | 2023-12-05 |
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| CN202311114667.XA Pending CN117159369A (en) | 2023-08-31 | 2023-08-31 | Finger pressure hemostasis point positioning method and device |
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