CN110504027A - A kind of X-Ray rabat pneumonia intelligent diagnosis system and method based on deep learning - Google Patents
A kind of X-Ray rabat pneumonia intelligent diagnosis system and method based on deep learning Download PDFInfo
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Abstract
The present invention discloses a kind of X-Ray rabat pneumonia intelligent diagnosis system and method based on deep learning, belong to Medical image classification technology field, the system includes that user logs in and registration module, picture format conversion module and pneumonia prediction module, with B/S framework, divide the system into four layers, respectively podium level, supporting layer, service layer and application layer.User installation process is simple, mitigates the dependence to home environment.By friendly interface and the system module designed around actual demand, the fusion of this system and other systems progress functionally can be made.X-Ray rabat pneumonia intelligent diagnosing method based on deep learning enhances transfer learning by data and the convergence rate that means accelerate model training such as improves to network structure, improves the accuracy rate of pneumonia identification and the generalization ability of model.System and method for of the invention can significantly reduce artificial diagosis and perplex to doctor's bring.
Description
Technical field
The present invention relates to Medical image classification technology field more particularly to a kind of X-Ray rabat lungs based on deep learning
Scorching intelligent diagnosis system and method.
Background technique
Medical image is one of information important in medical diagnosis on disease, and wherein imaging technique includes: computer tomography skill
Art (Computed Tomography, CT), positron emission tomography (Positron Emission Tomography,
PET), single photon emission tomographic imaging, Magnetic resonance imaging, ultrasonic imaging etc., traditional radiodiagnosis, which is still important, to be put
Penetrate one of diagnosis basis.Thoracic cavity is called the mirror of human health and disease, because it contains many kinds of important groups of human body
Knit structure, can provide human body various information, as the diagnosis of pulmonary disease, fracture of rib and damage, Heart enlargement symptom,
Cardiopulmonary coefficient etc. all can be identified and be confirmed by X-Ray rabat.Chest x-ray is still the routine inspection project of hospital image diagnosis,
Even if the case for needing CT, MRI to check, also often needs to refer to the performance of chest X-ray.X-Ray rabat is with its cheap valence
Lattice and faint radiological dose account for 40% or more of all irradiation image diagnosis.This embody X-Ray rabat in medical domain
Significant application value.
But how to analyze rabat is an extremely challenging task, even veteran expert also often feels
To intractable.As depth learning technology in recent years is in the huge hair in the fields such as computer vision and image classification, segmentation, identification
Exhibition, scientific research personnel propose many aided diagnosis methods in terms of the computer diagnosis of X-Ray rabat, and Aramato etc. is proposed
It is a kind of that area-of-interest is extracted based on the method for the wealthy value of more gray values, then letter is carried out using nine features of area-of-interest
Tubercle is extracted in single classification.Awai et al. positions Lung neoplasm using morphological method.Li et al. people continues the research achievement of sato,
And devise can enhance it is specific move back the performance being woven on image and weaken the Selective long-range DEPT function that remaining tissue shows, this
Sample makes area-of-interest more obvious.Pranav et al. proposes a kind of network C heXNet based on deep learning can be right
Lung X-ray-Ray image carries out auxiliary diagnosis.Guan et al. is in such a way that deep learning merges attention model come to lung X-ray-
Ray image carries out auxiliary diagnosis.Above-mentioned is the correlative study for pulmonary nodule mostly, even if having for pneumonia automatic identification
Lung X-ray-the Ray that correlative study is also based on deep learning classifies more, lacks specific aim to the intelligent diagnostics of pneumonia, additionally due to
Pneumonia focal area is easy to be blocked by normal institutional framework, so that subtle pneumonia lesion and institutional framework are difficult to differentiate between,
Lesion testing result is also easy to omit pneumonia lesion.It tends not to reach higher using traditional computer-aided diagnosis means
Accuracy rate.
Summary of the invention
In view of the above shortcomings of the prior art, the present invention provides a kind of X-Ray rabat pneumonia intelligence based on deep learning
Diagnostic system and method.
In order to solve the above technical problems, the technical solution used in the present invention is: a kind of X-Ray chest based on deep learning
Piece pneumonia intelligent diagnosis system, system include that three modules are respectively as follows: user's login and registration module, picture format conversion module
It is as shown in Figure 2 with the brief operation flow of pneumonia prediction module, system；
Wherein, user, which logs in, provides login function, registering functional and resetting cryptographic function with registration module, for user
The entrance for entering system is provided, process is as shown in Figure 3；
The login function needs user to input account and password in login interface and submits to system, System Back-end according to
The account of input inquires corresponding password into the user message table of database, if returned the result as sky, illustrates that user is defeated
The account entered is not present, if entered passwords does not match with user for the result returned, illustrates user password input error, only
Have when the password of user's input and the password inquired from database match, system can just be shown jumps interface accordingly；
The registering functional is that user can input account, password, phone, email address and register, and can be passed through from the background
JS script carries out validity judgement to these information, increases one newly in the user message table of database if information is all legal
Data；
The resetting cryptographic function is can to give password page input account and mailbox for change when user forgets Password
Verification information, when mailbox verification information is correct, system can allow user to carry out password resetting, and modify phase in user message table
The encrypted message answered.
Wherein, picture format conversion module, which provides, selects local picture and format conversion function, and format conversion function is used
In the dicom formatted data of image center is converted to jpeg format needed for this system, process is as shown in Figure 4.
Wherein, pneumonia prediction module provides patient information addition and picture prediction function, for carrying out patient information
Corresponding image data is simultaneously added in patients database by typing, then using trained prediction model in this system to this
The data of patient carry out forecast analysis, and process is as shown in Figure 5；
Patient information addition is user by by patient's name, gender, age, medical date input system, finally
Picture is added in patient information, JS verification can be carried out to these data from the background, saved later into database.
The picture prediction function is one anticipatory remark of selection ground picture, is completed by prediction model trained in system pair
Whether patient suffers from the prediction of pneumonia.
The system uses B/S framework, divides the system into four layers, respectively podium level, supporting layer, service layer and application layer,
Its framework is as shown in Figure 1；
Wherein, application layer includes system call interfaces, web access interface and result visualization interface, is connected with user terminal
It connects；
Service layer includes user's registration, user authentication, patient imports, picture imports, user logs in, format conversion, model
The user operable interface of load and picture prediction；Wherein, user's registration, user's login, user authentication belong to login and registration
The service that module provides；Format conversion belongs to the service of picture format conversion module offer；Patient imports, picture imports, model
Load, picture prediction belong to the service of pneumonia prediction module offer；
Supporting layer includes the classification method based on depth convolutional neural networks, the data base administration based on relevant database
System, traditional images processing method, medical image processing method；Classification method, biography wherein based on depth convolutional neural networks
System image processing method provides service for picture prediction；Data base management system based on relevant database be user's registration,
User logs in, user authentication provides service；Medical image processing method provides service for format conversion；
Podium level use Keras frame, according to convolutional layer, BN layers, advanced activation primitive layer, pond layer, full articulamentum with
And softmax layers be designed depth convolutional neural networks, selects loss function binary cross-entropy and optimization
Function RMSProp optimizes convolutional neural networks；Data depositary management using sqlite3 relevant database as this system
Reason system；Platform using ITK software library as medical image processing method；Using opencv computer vision library as tradition
The platform of image processing method.
In order to solve the above technical problems, the technical solution used in the present invention is: a kind of use the X- based on deep learning
The method that Ray rabat pneumonia intelligent diagnosis system is diagnosed, the process of this method is as shown in fig. 6, include the following steps:
Step 1: obtaining the X-Ray rabat image of a dicom format, the ReadImage method in the library ITK is called to read
Dicom format picture, later by calling the pixel square in ITK in GetArrayFromImage method extraction dicom image
Battle array, finally by the imwrite method in opencv by extracted picture element matrix preservation at the picture of jpeg format；
Step 2: selection data set Chest X-Ray Images (Pneumonia) generates training set and test set；
Step 3: depth convolutional neural networks VGG prediction model is established, as shown in fig. 7, wherein VGG model includes six volumes
Lamination, BN layers, advanced activation primitive, two full articulamentums and last softmax layer, and model training the number of iterations is set
The value of epoch；
Transfer learning is carried out to model using the trained weight of ImageNet for the weight load of model, in convolutional layer
BatchNormalization layers are added between activation primitive to accelerate network convergence rate, advanced activation primitive
LeakyRelu replaces Relu, excellent using cross entropy loss function binary cross-entropy as model optimization index
Change function and model convergence is accelerated using RMSProp method；
Step 4: VGG prediction model being trained with training set；
Step 5: using the method for overturning, rotation, the enhancing of these data of affine transformation, the image data of training set being carried out
Enhancing processing, obtains new training set；
Step 6: input test collection tests trained VGG prediction model, obtains predictablity rate；
Step 7: step 4 is repeated to step 7, and training is iterated to depth convolutional neural networks VGG prediction model,
Until the number of iterations reaches the value of preset epoch, stopping iteration；
Step 8: the highest VGG model of accuracy rate on test set is saved；
Step 9: the picture for the jpeg format that step 1 obtains is input to the highest VGG model of accuracy rate that step 8 saves
In, obtain the classification prediction result of picture.
The beneficial effects of adopting the technical scheme are that
1, the present invention is compared to traditional deep learning algorithm, by data enhancing, transfer learning and to network structure
It the convergence rate that means accelerate model training such as improves, improves the accuracy rate of pneumonia identification and the extensive energy of model
Power；
2, the BS application based on Django that present invention provides one, simplifies user installation process, mitigates to local ring
The dependence in border.By friendly interface and the system module designed around actual demand, can this system be easy to
Ground is received by each hospital, can also be easy to and the other systems of hospital carry out fusion functionally.Based on MVC framework point
Application also improve the efficiency of later maintenance and redevelopment；
3, by intelligent auxiliary diagnosis system, artificial diagosis can also be significantly reduced and perplexed to doctor's bring.
Detailed description of the invention
Fig. 1 is a kind of X-Ray rabat pneumonia intelligent diagnosis system architecture diagram based on deep learning of the present invention；
Fig. 2 is a kind of brief business process map of X-Ray rabat pneumonia intelligent diagnosis system based on deep learning of the present invention；
Fig. 3 is that user of the present invention logs in and registration module flow chart；
Fig. 4 is picture format conversion module flow chart of the present invention；
The position Fig. 5 pneumonia prediction module flow chart of the present invention；
Fig. 6 is a kind of X-Ray rabat pneumonia intelligent diagnosing method flow chart based on deep learning of the present invention；
Fig. 7 is depth convolutional neural networks VGG prediction model structure chart of the present invention；
Fig. 8 is user's login interface figure in the embodiment of the present invention；
Fig. 9 is user's registration surface chart in the embodiment of the present invention；
Figure 10 is picture format transition interface figure in the embodiment of the present invention；
Figure 11 is to add patient information in the embodiment of the present invention and upload patient X-Ray rabat and carry out prediction interface figure；
Figure 12 is patient's X-Ray rabat pneumonia prediction result output interface in the embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiments of the present invention will be described in further detail.Implement below
Example is not intended to limit the scope of the invention for illustrating the present invention.
A kind of X-Ray rabat pneumonia intelligent diagnosis system based on deep learning, system include that three modules are respectively as follows: use
Family logs in and registration module, picture format conversion module and pneumonia prediction module, and the brief operation flow of system is as shown in Figure 2；
Wherein, user, which logs in, provides login function, registering functional and resetting cryptographic function with registration module, for user
The entrance for entering system is provided, process is as shown in Figure 3；
The login function needs user to input account and password in login interface and submits to system, user's login interface
As shown in figure 8, account of the System Back-end according to input, corresponding password is inquired into the user message table of database, if returned
Returning result is sky, then illustrates that the account of user's input is not present, if returning the result of goods, entered passwords does not match with user,
Illustrate user password input error, only when the password of user's input and the password inquired from database match, system
It can just show and jump interface accordingly；
The registering functional is that user can input account, password, phone, email address and register, user's registration circle
Face is as shown in figure 9, backstage can carry out validity judgement to these information by JS script, in data if information is all legal
Increase a data in the user message table in library newly；
The resetting cryptographic function is can to give password page input account and mailbox for change when user forgets Password
Verification information, when mailbox verification information is correct, system can allow user to carry out password resetting, and modify phase in user message table
The encrypted message answered.
Wherein, picture format conversion module, which provides, selects local picture and format conversion function, and picture format converts boundary
Face is as shown in Figure 10, and format conversion function is used to the dicom formatted data of image center being converted to jpeg lattice needed for this system
Formula.
Wherein, pneumonia prediction module provides patient information addition and picture prediction function, for carrying out patient information
Corresponding image data is simultaneously added in patients database by typing, is added patient information and is uploaded patient's X-Ray rabat and goes forward side by side
Row prediction interface is as shown in figure 11, then carries out prediction point using data of the prediction model trained in this system to the patient
Analysis；
Patient information addition is user by by patient's name, gender, age, medical date input system, finally
It is added to picture in patient information, JS verification can be carried out to these data from the background, saved later into database.
The picture prediction function is one anticipatory remark of selection ground picture, is completed by prediction model trained in system pair
Whether patient suffers from the prediction of pneumonia.
The system uses B/S framework, divides the system into four layers, respectively podium level, supporting layer, service layer and application layer,
Its framework is as shown in Figure 1；
Wherein, application layer includes system call interfaces, web access interface and result visualization interface, is connected with user terminal
It connects；
Service layer includes user's registration, user authentication, patient imports, picture imports, user logs in, format conversion, model
The user operable interface of load and picture prediction；Wherein, user's registration, user's login, user authentication belong to login and registration
The service that module provides；Format conversion belongs to the service of picture format conversion module offer；Patient imports, picture imports, model
Load, picture prediction belong to the service of pneumonia prediction module offer；
Supporting layer includes the classification method based on depth convolutional neural networks, the data base administration based on relevant database
System, traditional images processing method, medical image processing method；Classification method, biography wherein based on depth convolutional neural networks
System image processing method provides service for picture prediction；Data base management system based on relevant database be user's registration,
User logs in, user authentication provides service；Medical image processing method provides service for format conversion；
Podium level use Keras frame, according to convolutional layer, BN layers, advanced activation primitive layer, pond layer, full articulamentum with
And softmax layers be designed depth convolutional neural networks, selects loss function binary cross-entropy and optimization
Function RMSProp optimizes convolutional neural networks；Data depositary management using sqlite3 relevant database as this system
Reason system；Platform using ITK software library as medical image processing method；Using opencv computer vision library as tradition
The platform of image processing method.
In order to solve the above technical problems, the technical solution used in the present invention is: a kind of use the X- based on deep learning
The method that Ray rabat pneumonia intelligent diagnosis system is diagnosed, the process of this method is as shown in fig. 6, include the following steps:
Step 1: obtaining the X-Ray rabat image of a dicom format, the ReadImage method in the library ITK is called to read
Dicom format picture, later by calling the pixel square in ITK in GetArrayFromImage method extraction dicom image
Battle array, finally by the imwrite method in opencv by extracted picture element matrix preservation at the picture of jpeg format；
Step 2: selection data set Chest X-Ray Images (Pneumonia) generates training set and test set；
Step 3: depth convolutional neural networks VGG prediction model is established, as shown in fig. 7, wherein VGG model includes six volumes
Model training the number of iterations is arranged in lamination, BN layers, advanced activation primitive, two full articulamentums and last softmax layer
Epoch=50；
Transfer learning is carried out to model using the trained weight of ImageNet for the weight load of model, in convolutional layer
BatchNormalization layers are added between activation primitive to accelerate network convergence rate, advanced activation primitive
LeakyRelu replaces Relu, excellent using cross entropy loss function binary cross-entropy as model optimization index
Change function and model convergence is accelerated using RMSProp method；
Step 4: VGG prediction model being trained with training set；
Step 5: using the method for overturning, rotation, the enhancing of these data of affine transformation, the image data of training set being carried out
Enhancing processing, obtains new training set；
Step 6: input test collection tests trained VGG prediction model, obtains predictablity rate；
Step 7: step 4 is repeated to step 7, and training is iterated to depth convolutional neural networks VGG prediction model,
Until the number of iterations reaches the value of preset epoch, stopping iteration；
Step 8: the highest VGG model of accuracy rate on test set is saved；
Step 9: the picture for the jpeg format that step 1 obtains is input to the highest VGG model of accuracy rate that step 8 saves
In, obtain the classification prediction result of picture.
Prediction result is arranged in the present embodiment and exports rule: 1 is pneumonia, and 0 is normal；
The X-Ray rabat image prediction result output of patient is 0 in the present embodiment, can determine whether that the patient does not suffer from an inflammation of the lungs,
As a result output interface is as shown in figure 12.
Claims (6)
1. a kind of X-Ray rabat pneumonia intelligent diagnosis system based on deep learning, it is characterised in that log in and infuse including user
Volume module, picture format conversion module and pneumonia prediction module, the system use B/S framework, divide the system into four layers, respectively
Podium level, supporting layer, service layer and application layer；
Wherein, application layer includes system call interfaces, web access interface and result visualization interface, is connected with user terminal；
Service layer includes user's registration, user authentication, patient imports, picture imports, user logs in, format conversion, model load
With the user operable interface of picture prediction；Wherein, user's registration, user's login, user authentication belong to login and registration module
The service of offer；Format conversion belongs to the service of picture format conversion module offer；Patient imports, picture imports, model loads,
Picture prediction belongs to the service of pneumonia prediction module offer；
Supporting layer includes the classification method based on depth convolutional neural networks, the data base administration system based on relevant database
System, traditional images processing method, medical image processing method；Classification method, tradition wherein based on depth convolutional neural networks
Image processing method provides service for picture prediction；Data base management system based on relevant database is user's registration, uses
Family logs in, user authentication provides service；Medical image processing method provides service for format conversion；
Podium level use Keras frame, according to convolutional layer, BN layers, advanced activation primitive layer, pond layer, full articulamentum and
Softmax layers are designed depth convolutional neural networks, select loss function binary cross-entropy and optimization letter
Number RMSProp optimizes convolutional neural networks；Data base administration using sqlite3 relevant database as this system
System；Platform using ITK software library as medical image processing method；Using opencv computer vision library as tradition figure
As the platform of processing method.
2. a kind of X-Ray rabat pneumonia intelligent diagnosis system based on deep learning according to claim 1, feature exist
It is logged in user and provides login function, registering functional and resetting cryptographic function with registration module, enter system for providing user
The entrance of system；
The login function needs user to input account and password in login interface and submits to system, and System Back-end is according to input
Account, corresponding password is inquired into the user message table of database, if returned the result as sky, illustrates user's input
Account is not present, if entered passwords does not match with user for the result returned, illustrates user password input error, only when
When the password of user's input and the password inquired from database match, system can just be shown jumps interface accordingly；
The registering functional is that user can input account, password, phone, email address and register, and can pass through JS foot from the background
This carries out validity judgement to these information, increases a number newly in the user message table of database if information is all legal
According to；
The resetting cryptographic function be when user forgets Password, can give for change the password page input account and mailbox verifying
Information, when mailbox verification information is correct, system can allow user to carry out password resetting, and modify corresponding in user message table
Encrypted message.
3. a kind of X-Ray rabat pneumonia intelligent diagnosis system based on deep learning according to claim 1, feature exist
It is provided in picture format conversion module and selects local picture and format conversion function, format conversion function is used for image center
Dicom formatted data be converted to jpeg format needed for this system.
4. a kind of X-Ray rabat pneumonia intelligent diagnosis system based on deep learning according to claim 1, feature exist
Patient information addition and picture prediction function are provided in pneumonia prediction module, for patient information to be carried out typing and will be corresponded to
Image data be added in patients database, then using trained prediction model in this system to the data of the patient into
Row forecast analysis；
The patient information addition is user by will finally scheme patient's name, gender, age, medical date input system
Piece is added in patient information, can be carried out JS verification to these data from the background, be saved later into database；
The picture prediction function is one anticipatory remark of selection ground picture, is completed by prediction model trained in system to patient
Whether the prediction of pneumonia is suffered from.
5. a kind of X-Ray rabat pneumonia intelligent diagnosis system using described in claim 1 based on deep learning is diagnosed
Method, it is characterised in that include the following steps:
Step 1: obtaining the X-Ray rabat image of a dicom format, the ReadImage method in the library ITK is called to read
Dicom format picture, later by calling the pixel square in ITK in GetArrayFromImage method extraction dicom image
Battle array, finally by the imwrite method in opencv by extracted picture element matrix preservation at the picture of jpeg format；
Step 2: selection data set Chest X-Ray Images (Pneumonia) generates training set and test set；
Step 3: establish depth convolutional neural networks VGG prediction model, wherein VGG model include six convolutional layers, BN layers, it is advanced
Activation primitive, two full articulamentums and last softmax layer, and the value of model training the number of iterations epoch is set；
Step 4: VGG prediction model being trained with training set；
Step 5: using the method for overturning, rotation, the enhancing of these data of affine transformation, the image data of training set being enhanced
Processing, obtains new training set；
Step 6: input test collection tests trained VGG prediction model, obtains predictablity rate；
Step 7: step 4 is repeated to step 7, and training is iterated to depth convolutional neural networks VGG prediction model, until
The number of iterations reaches the value of preset epoch, stops iteration；
Step 8: the highest VGG model of accuracy rate on test set is saved；
Step 9: the picture for the jpeg format that step 1 obtains is input in the highest VGG model of accuracy rate that step 8 saves,
Obtain the classification prediction result of picture.
6. a kind of X-Ray rabat pneumonia intelligence using described in claim 1 based on deep learning according to claim 5
The method that energy diagnostic system is diagnosed, it is characterised in that depth convolutional neural networks VGG prediction mould is established in the step 3
Type carries out transfer learning to model using the trained weight of ImageNet for the weight load of model, in convolutional layer and swashs
BatchNormalization layers are added between function living to accelerate network convergence rate, advanced activation primitive LeakyRelu generation
For Relu, using cross entropy loss function binary cross-entropy as model optimization index, majorized function is used
RMSProp method accelerates model convergence.
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| CN201910767278.4A CN110504027A (en) | 2019-08-20 | 2019-08-20 | A kind of X-Ray rabat pneumonia intelligent diagnosis system and method based on deep learning |
| PCT/CN2019/107225 WO2021031279A1 (en) | 2019-08-20 | 2019-09-23 | Deep-learning-based intelligent pneumonia diagnosis system and method for x-ray chest radiograph |
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| CN201910767278.4A CN110504027A (en) | 2019-08-20 | 2019-08-20 | A kind of X-Ray rabat pneumonia intelligent diagnosis system and method based on deep learning |
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| CN113476065A (en) * | 2021-09-06 | 2021-10-08 | 华南理工大学 | Multiclass pneumonia diagnostic system |
| CN114821826A (en) * | 2022-02-03 | 2022-07-29 | 北京泽桥医疗科技股份有限公司 | Visual system for interactive three-dimensional human body medical model |
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