CN108364025A - Gastroscope image-recognizing method, device, equipment and medium based on deep learning - Google Patents
Gastroscope image-recognizing method, device, equipment and medium based on deep learning Download PDFInfo
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- CN108364025A CN108364025A CN201810145240.9A CN201810145240A CN108364025A CN 108364025 A CN108364025 A CN 108364025A CN 201810145240 A CN201810145240 A CN 201810145240A CN 108364025 A CN108364025 A CN 108364025A
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Abstract
The invention discloses a kind of gastroscope image-recognizing method, device, terminal device and computer readable storage medium based on deep learning, method include the following steps：The gastroscope image of acquisition is pre-processed；According to pretreated gastroscope image, the identification of type attribute is carried out by gastroscope identification model；Assisting in diagnosis and treatment report is generated according to the recognition result of the gastroscope identification model, realizes the diagnostic result of profession, quick diagnosis efficiency, to improve primary care technology.
Description
Technical field
The present invention relates to computer field of medical technology more particularly to a kind of gastroscope image recognition sides based on deep learning
Method, device, terminal device and computer readable storage medium.
Background technology
It is present in the prior art, the gastroscope image that doctor is obtained by gastroscope, for by gastroscope image to patient
The tentative diagnosis of the state of an illness, there is still a need for doctors to see one by one for conventional method, and the disease of patient is tentatively judged by the observation of doctor
Feelings, one side efficiency is relatively low, on the other hand sometimes can not accurately judge the state of an illness according to the diagnostician of gastroscope picture.
Invention content
In view of the above-mentioned problems, the gastroscope image-recognizing method that the purpose of the present invention is to provide a kind of based on deep learning,
Device, terminal device and computer readable storage medium realize the diagnostic result of profession, quick diagnosis efficiency, to improve
Primary care technology.
In a first aspect, an embodiment of the present invention provides a kind of gastroscope image-recognizing method based on deep learning, including with
Lower step：
The gastroscope image of acquisition is pre-processed；
According to pretreated gastroscope image, the identification of type attribute is carried out by gastroscope identification model；
Assisting in diagnosis and treatment report is generated according to the recognition result of the gastroscope identification model.
In the first realization method of first aspect, further include：
The gastroscope identification model is built by convolutional neural networks algorithm.
In second of realization method of first aspect, the type attribute includes gastric cancer, and gastric ulcer and stomach are normal.
In the third realization method of first aspect, further include：
Obtain the gastroscope image that client uploads.
Second aspect, an embodiment of the present invention provides a kind of gastroscope pattern recognition device based on deep learning, including：
Pretreatment unit is pre-processed for the gastroscope image to acquisition；
Recognition unit, for according to pretreated gastroscope image, the knowledge of type attribute to be carried out by gastroscope identification model
Not；
As a result output unit, for generating assisting in diagnosis and treatment report according to the recognition result of the gastroscope identification model.
In the first realization method of first aspect, the gastroscope is built by convolutional neural networks algorithm and identifies mould
Type.
In second of realization method of first aspect, the type attribute includes gastric cancer, and gastric ulcer and stomach are normal.
In the third realization method of first aspect, further include：
Image acquisition unit, the gastroscope image for obtaining client upload.
The third aspect, an embodiment of the present invention provides a kind of gastroscope image recognition terminal device based on deep learning, packet
It includes processor, memory and is stored in the memory and is configured as the computer program executed by the processor,
The processor realized when executing the computer program it is any one of above-mentioned described in the gastroscope image based on deep learning
Recognition methods.
Fourth aspect, an embodiment of the present invention provides a kind of computer readable storage medium, the computer-readable storage
Medium includes the computer program of storage, wherein controls the computer-readable storage medium when the computer program is run
Equipment where matter execute it is any one of above-mentioned described in the gastroscope image-recognizing method based on deep learning.
The gastroscope image-recognizing method that an embodiment of the present invention provides a kind of based on deep learning, device, terminal device and
Computer readable storage medium, one embodiment have the advantages that：
The gastroscope image of acquisition is pre-processed first, then according to pretreated gastroscope image, is known by gastroscope
Other model carries out the identification of type attribute, finally generates diagnosis and treatment report according to the recognition result of the gastroscope identification model, realizes
Diagnostic result, quick diagnosis efficiency and the easily application mode of profession are promoted to improve primary care technology in digestion
The homogeneity of mirror doctor is horizontal, improves the recall rate of alimentary canal morning cancer.
Description of the drawings
In order to illustrate more clearly of technical scheme of the present invention, attached drawing needed in embodiment will be made below
Simply introduce, it should be apparent that, the accompanying drawings in the following description is only some embodiments of the present invention, general for this field
For logical technical staff, without creative efforts, other drawings may also be obtained based on these drawings.
Fig. 1 is the flow signal for the gastroscope image-recognizing method based on deep learning that first embodiment of the invention provides
Figure.
Fig. 2 is the structural representation for the gastroscope pattern recognition device based on deep learning that third embodiment of the invention provides
Figure.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
It, can be by referring to Fig. 1, an embodiment of the present invention provides a kind of gastroscope image-recognizing method based on deep learning
Terminal device executes, and includes the following steps：
S11 pre-processes the gastroscope image of acquisition.
In embodiments of the present invention, the terminal device can be desktop PC, notebook, palm PC and high in the clouds
The computing devices such as server.
In embodiments of the present invention, the pretreatment of data refer to classified to collected data or be grouped before done examine
The necessary processing such as core, screening, normalization, the terminal device pre-process the gastroscope image of acquisition, to reach enhancing
The purpose of data.
S12 carries out the identification of type attribute by gastroscope identification model according to pretreated gastroscope image.
In embodiments of the present invention, the terminal device obtains pretreated gastroscope image, then according to pretreatment after
Gastroscope image, by gastroscope identification model carry out type attribute identification, the type attribute includes gastric cancer, gastric ulcer and stomach
Normally, the gastroscope identification model is built by convolutional neural networks algorithm, specifically, passes through Inception_v3 model constructions
Gastroscope identification model, Inception_v3 are a kind of convolutional neural networks (CNN), contain one by convolutional layer and sub-sampling layer
The feature extractor of composition.In the convolutional layer of convolutional neural networks, a neuron is only connect with part adjacent bed neuron.It is logical
Include often several characteristic planes (featureMap), each characteristic plane is made of the neuron of some rectangular arrangeds, together
The neuron of one characteristic plane shares weights, and shared weights are exactly convolution kernel here.Inception_v3 is with respect to other convolution
Big convolution is resolved into small convolution, reduces parameter amount, mitigate over-fitting, increases the nonlinear ability to express of network by neural network.
Convolutional network allows dimension of picture to be gradually reduced from output is input to, and output channel number gradually increases, that is, allows space structure, will
Spatial information is converted into the characteristic information of Higher Order Abstract.The thinking of the high-order feature of different level of abstractions is extracted with multiple branches very
Effectively, the ability to express of network can be enriched, the parameter in Inception_v3 networks is from ImageNet image data collection
Upper training and the parameter come, change softmax layer, finely tune the parameter of last layer, splitting data into three classification, (gastric cancer, stomach are routed
Ulcer, stomach are normal).
S13 generates diagnosis and treatment report according to the recognition result of the gastroscope identification model.
In embodiments of the present invention, the terminal device generates auxiliary according to the recognition result of the gastroscope identification model and examines
Report is treated, diagnosis and treatment judgement is carried out with assist digestion scope doctor.
In conclusion first embodiment of the invention provides a kind of gastroscope image-recognizing method based on deep learning, it is first
First the gastroscope image of acquisition is pre-processed, then according to pretreated gastroscope image, is carried out by gastroscope identification model
The identification of type attribute finally generates assisting in diagnosis and treatment report according to the recognition result of the gastroscope identification model, realizes profession
Diagnostic result, quick diagnosis efficiency and easily application mode promote digestive endoscopy doctor to improve primary care technology
Homogeneity it is horizontal, improve the recall rate of alimentary canal morning cancer.
In order to facilitate the understanding of the present invention, some currently preferred embodiments of the present invention will be done and will further be retouched below
It states.
Second embodiment of the invention：
On the basis of first embodiment of the invention, further include：
Obtain the gastroscope image that client uploads.
In embodiments of the present invention, model file is transplanted on Java web, user only needs log-on webpage, can upload
Gastroscope image, the terminal device receive the gastroscope image that client uploads, are identified by gastroscope identification model, you can
To the tentative prediction result of gastroscope image.
Referring to Fig. 2, third embodiment of the invention provides a kind of gastroscope pattern recognition device based on deep learning, packet
It includes：
Pretreatment unit 11 is pre-processed for the gastroscope image to acquisition.
Recognition unit 12, for according to pretreated gastroscope image, type attribute to be carried out by gastroscope identification model
Identification.
As a result output unit 13, for generating diagnosis and treatment satellite report according to the recognition result of the gastroscope identification model.
In the first realization method of 3rd embodiment, the gastroscope is built by convolutional neural networks algorithm and identifies mould
Type.
In second of realization method of 3rd embodiment, the type attribute includes gastric cancer, and gastric ulcer and stomach are normal.
In the third realization method of 3rd embodiment, further include：
Image acquisition unit, the gastroscope image for obtaining client upload.
Fourth embodiment of the invention provides a kind of gastroscope image recognition terminal device based on deep learning.The embodiment
The gastroscope image recognition terminal device based on deep learning include：It processor, memory and is stored in the memory
And the computer program that can be run on the processor, such as the gastroscope image recognition program based on deep learning.The place
Reason device is realized when executing the computer program in above-mentioned each gastroscope image-recognizing method embodiment based on deep learning
Step, such as step S11 shown in FIG. 1.Alternatively, the processor realizes that above-mentioned each device is real when executing the computer program
Apply the function of each module/unit in example, such as pretreatment unit.
Illustratively, the computer program can be divided into one or more module/units, one or more
A module/unit is stored in the memory, and is executed by the processor, to complete the present invention.It is one or more
A module/unit can be the series of computation machine program instruction section that can complete specific function, and the instruction segment is for describing institute
State implementation procedure of the computer program in the gastroscope image recognition terminal device based on deep learning.
The gastroscope image recognition terminal device based on deep learning can be desktop PC, notebook, palm
The computing devices such as computer and cloud server.The gastroscope image recognition terminal device based on deep learning may include, but not
It is only limitted to, processor, memory.It will be understood by those skilled in the art that above-mentioned component is only based on the gastroscope of deep learning
The example of image recognition terminal device does not constitute the restriction to the gastroscope image recognition terminal device based on deep learning, can
To include either combining certain components or different components, such as described based on depth than above-mentioned more or fewer components
The gastroscope image recognition terminal device of study can also include input-output equipment, network access equipment, bus etc..
Alleged processor can be central processing unit (Central Processing Unit, CPU), can also be it
His general processor, digital signal processor (Digital Signal Processor, DSP), application-specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor can also be any conventional processor
Deng the processor is the control centre of the gastroscope image recognition terminal device based on deep learning, utilizes various interfaces
The entire various pieces of the gastroscope image recognition terminal device based on deep learning with connection.
The memory can be used for storing the computer program and/or module, and the processor is by running or executing
Computer program in the memory and/or module are stored, and calls the data being stored in memory, described in realization
The various functions of gastroscope image recognition terminal device based on deep learning.The memory can mainly include storing program area and
Storage data field, wherein storing program area can storage program area, application program (such as the sound needed at least one function
Playing function, image player function etc.) etc.；Storage data field can be stored uses created data (such as sound according to mobile phone
Frequency evidence, phone directory etc.) etc..In addition, memory may include high-speed random access memory, can also include non-volatile deposit
Reservoir, such as hard disk, memory, plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital
(Secure Digital, SD) block, flash card (Flash Card), at least one disk memory, flush memory device or other
Volatile solid-state part.
Wherein, if the integrated module/unit of the gastroscope image recognition terminal device based on deep learning is with software
The form of functional unit is realized and when sold or used as an independent product, can be stored in a computer-readable storage
In medium.Based on this understanding, the present invention realizes all or part of flow in above-described embodiment method, can also pass through meter
Calculation machine program is completed to instruct relevant hardware, and the computer program can be stored in a computer readable storage medium
In, the computer program is when being executed by processor, it can be achieved that the step of above-mentioned each embodiment of the method.Wherein, the calculating
Machine program includes computer program code, and the computer program code can be source code form, object identification code form, can hold
Style of writing part or certain intermediate forms etc..The computer-readable medium may include：The computer program code can be carried
Any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic disc, CD, computer storage, read-only memory (ROM,
Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunications letter
Number and software distribution medium etc..It should be noted that the content that the computer-readable medium includes can be managed according to the administration of justice
Local legislation and the requirement of patent practice carry out increase and decrease appropriate, such as in certain jurisdictions, according to legislation and patent
Practice, computer-readable medium do not include electric carrier signal and telecommunication signal.
It should be noted that the apparatus embodiments described above are merely exemplary, wherein described be used as separating component
The unit of explanation may or may not be physically separated, and the component shown as unit can be or can also
It is not physical unit, you can be located at a place, or may be distributed over multiple network units.It can be according to actual
It needs that some or all of module therein is selected to achieve the purpose of the solution of this embodiment.In addition, device provided by the invention
In embodiment attached drawing, the connection relation between module indicates there is communication connection between them, specifically can be implemented as one or
A plurality of communication bus or signal wire.Those of ordinary skill in the art are without creative efforts, you can to understand
And implement.
The above is the preferred embodiment of the present invention, it is noted that for those skilled in the art
For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (10)
1. a kind of gastroscope image-recognizing method based on deep learning, which is characterized in that include the following steps：
The gastroscope image of acquisition is pre-processed；
According to pretreated gastroscope image, the identification of type attribute is carried out by gastroscope identification model；
Assisting in diagnosis and treatment report is generated according to the recognition result of the gastroscope identification model.
2. the gastroscope image-recognizing method according to claim 1 based on deep learning, which is characterized in that pass through convolution god
The gastroscope identification model is built through network algorithm.
3. the gastroscope image-recognizing method according to claim 1 based on deep learning, which is characterized in that the type category
Property includes gastric cancer, and gastric ulcer and stomach are normal.
4. the gastroscope image-recognizing method according to claim 1 based on deep learning, which is characterized in that further include：
Obtain the gastroscope image that client uploads.
5. a kind of gastroscope pattern recognition device based on deep learning, which is characterized in that including：
Pretreatment unit is pre-processed for the gastroscope image to acquisition；
Recognition unit, for according to pretreated gastroscope image, the identification of type attribute to be carried out by gastroscope identification model；
As a result output unit, for generating assisting in diagnosis and treatment report according to the recognition result of the gastroscope identification model.
6. the gastroscope pattern recognition device according to claim 5 based on deep learning, which is characterized in that pass through convolution god
The gastroscope identification model is built through network algorithm.
7. the gastroscope pattern recognition device according to claim 5 based on deep learning, which is characterized in that the type category
Property includes gastric cancer, and gastric ulcer and stomach are normal.
8. the gastroscope pattern recognition device according to claim 5 based on deep learning, which is characterized in that further include：
Image acquisition unit, the gastroscope image for obtaining client upload.
9. a kind of gastroscope image recognition terminal device based on deep learning, including processor, memory and it is stored in described
In memory and it is configured as the computer program executed by the processor, when the processor executes the computer program
Realize the gastroscope image-recognizing method based on deep learning as described in any one of Claims 1-4.
10. a kind of computer readable storage medium, which is characterized in that the computer readable storage medium includes the calculating of storage
Machine program, wherein equipment where controlling the computer readable storage medium when the computer program is run is executed as weighed
Profit requires the gastroscope image-recognizing method based on deep learning described in any one of 1 to 4.
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| CN109658399A (en) * | 2018-12-13 | 2019-04-19 | 深圳先进技术研究院 | A kind of neck patch image-recognizing method and device |
| CN109949281A (en) * | 2019-03-11 | 2019-06-28 | 哈尔滨工业大学（威海） | A kind of gastroscope picture quality detection method and device |
| WO2020118618A1 (en) * | 2018-12-13 | 2020-06-18 | 深圳先进技术研究院 | Mammary gland mass image recognition method and device |
| CN112712515A (en) * | 2021-01-06 | 2021-04-27 | 重庆金山医疗器械有限公司 | Endoscope image processing method and device, electronic equipment and storage medium |
| CN112789686A (en) * | 2018-10-02 | 2021-05-11 | 翰林大学产学合作团 | Device and method for diagnosing stomach pathological changes by deep learning of stomach endoscope images |
| CN116071622A (en) * | 2023-04-06 | 2023-05-05 | 广州思德医疗科技有限公司 | Stomach image recognition model construction method and system based on deep learning |
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