CN109902734A - A kind of cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks - Google Patents
A kind of cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks Download PDFInfo
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Abstract
The present invention relates to a kind of the cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks, designed image processing, medical big data, deep learning field.Include: 1) to acquire medical image, constructs training set；2) image in training set is handled, training sample required for generating；3) the medical image recognition training based on deep learning is carried out using training sample, generates the quick identification model of trained medical image；4) new medical image, building verifying collection are acquired；5) image is concentrated to verify model using verifying.The invention avoids in current deep learning the problem of medical image data sets redundancy, and realize the quick identification of medical image, recognition speed is fast.
Description
Technical field
The present invention relates to a kind of cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks, belongs to
Artificial intelligence field
Background technique
Since convolution concept in 2012 is suggested, convolutional neural networks (abbreviation CNN) image classification, image segmentation,
The fields such as target detection are widely applied[11], especially with the emergence and development of field of wisdom medical treatment, the kind to diagnose the illness
Class increases, and the pathological relation complexity between disease increases, and is increasingly stringenter to the application demand of convolutional neural networks.As a result
Each Lu great Niu proposes the more superior CNN network of performance one after another, such as VGG, GoogLeNet, ResNet, DenseNet[12-15]Deng.By
In the property of neural network, in order to obtain better performance, the network number of plies is continuously increased, from 7 layers of AlexNet to 16 layers of VGG, then
From 22 layers of 16 layers of VGG to GoogLeNet, then to 152 layers of ResNet, more there are thousands of layers of ResNet and DenseNet.Although
Network performance is improved, but comes with efficiency.
Safe and efficient auxiliary doctor is createed in combination with big data and knowledge etc. by artificial intelligence deep learning
Diagnostic method, and then provide more effective help for the correct diagnosis and treatment of medical field, this is a kind of based on YOLO convolution mind
Cholelithiasis CT medical image method for quickly identifying through network.
It is not difficult to find out that in current medical field, the mode of traditional doctor's facial diagnosis is obtained by reading to medical image
The information of lesion, Misdiagnosis is serious, and be easy to cause the state of an illness to be delayed in medical low developed area；And it is a kind of based on YOLO volumes
The cholelithiasis CT medical image method for quickly identifying of product neural network assists medical treatment by means of AI algorithm, simplifies detection step
Suddenly, diagnosis efficiency is improved, Misdiagnosis rate is reduced.YOLO convolutional neural networks model can accelerate in medical field application
And intelligent medical treatment is pushed in the development in mobile terminal field, it realizes the interaction of internet, alleviates that medical resource area differentiation is big etc. to ask
Topic, so that " intelligent medical treatment " be made to be achieved and promote.
Summary of the invention
The purpose of the present invention is to solve difficulties present in above-mentioned medical field, provide a kind of based on YOLO convolution mind
Cholelithiasis CT medical image method for quickly identifying through network can help clinical doctor to carry out correct diagnosis and treatment, improve disease
The accuracy rate of disease diagnosis.
The present invention is to solve difficulty present in above-mentioned medical field, and used technical solution is: one kind is based on
The cholelithiasis CT medical image method for quickly identifying of YOLO convolutional neural networks.
A kind of cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks, which is characterized in that packet
It includes:
1) cholelithiasis CT medical image training set is constructed；
2) training set is handled, training sample required for generating；
3) the cholelithiasis CT medical image recognition training based on deep learning is carried out using training sample, generates cholelithiasis CT
The quick identification model of medical image；
4) it constructs new cholelithiasis CT medical image and verifies collection；
5) the trained quick identification model of cholelithiasis CT medical image is verified.
Further, the image in cholelithiasis CT medical image training set is analyzed, include the following steps in appoint
Anticipate one or more:
1) the step of image in the training set being zoomed in or out；
2) spin step is carried out to the image in the training set；
3) translation step is carried out to the image in the training set；
4) the step of radiation transformation being carried out to the image in the training set；
5) the step of image degree of comparing in the training set being enhanced；
6) the step of focal area label being carried out to the image in the training set.
A kind of further cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks, convolution
Neural network structure includes:
1) data preprocessing phase, including zooming in or out, rotate, translate and radiating to cholelithiasis CT medical image
Variation and etc., the pretreatment of complete paired data；
2) it in the local convolution stage, is operated including one-dimensional convolution sum pond etc., it is fast using YOLO convolutional neural networks speed
The characteristics of, the label behaviour of the focal area of contrast enhancement processing and cholelithiasis CT medical image to cholelithiasis CT medical image
Make；
3) global convolution stage, convolution and pondization operation including YOLO convolutional neural networks, to including marked region
Cholelithiasis CT medical image carries out feature extraction, then carries out at last identification in the full articulamentum for being transferred to network last layer
Reason.
A kind of further cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks, feature
It is to comprise the following modules:
1) input module, for inputting cholelithiasis CT medical image data sets；
2) analytic unit generates multiple training samples for handling the cholelithiasis CT medical image；
3) training assembly carries out the cholelithiasis CT medical image recognition training based on deep learning using training sample, raw
At the quick identification model of cholelithiasis CT medical image；
4) detection components, using the cholelithiasis CT medical image verifying collection newly constructed, to trained cholelithiasis CT medical treatment
The quick identification model of image is verified.
Advantages and advantages of the invention are as follows:
The present invention constantly rises for China's cholelithiasis illness rate, professional medical resource provision is insufficient, traditional cholelithiasis doctor
The problems such as image analysis effect is bad is treated, it is quick to provide a kind of cholelithiasis CT medical image based on YOLO convolutional neural networks
Recognition methods is realized using technologies such as cholelithiasis CT medical image data and deep learnings to cholelithiasis CT medical image
Automatic signature extraction and intelligent diagnostics, it is entire to diagnose process quickly, efficiently, improve the accuracy of cholelithiasis diagnosis.It is based on
The cholelithiasis CT medical image method for quickly identifying of YOLO convolutional neural networks, type and the form for solving cholelithiasis are a variety of more
Sample is so that difficult problem in its clinical diagnostic process.Relative to traditional autonomous diagnostic method of doctor, the present invention can be
In the extremely short time, the type, form and position of cholelithiasis are determined, and provide diagnostic result.
Detailed description of the invention
Fig. 1 is that a kind of cholelithiasis CT medical image based on YOLO convolutional neural networks of embodiment according to the present invention is fast
The flow diagram of fast recognition methods
Specific embodiment
Further technical solution of the present invention is illustrated below in conjunction with specific embodiment.
This system subject schemes major embodiment intelligent diagnostics, quickly, the high basic thought of accuracy rate.As shown in Figure 1, one
Cholelithiasis CT medical image method for quickly identifying of the kind based on YOLO convolutional neural networks, comprises the following modules:
1) input module, for inputting cholelithiasis CT medical image data sets；
2) analytic unit generates multiple training samples for handling the cholelithiasis CT medical image；
3) training assembly carries out the cholelithiasis CT medical image recognition training based on deep learning using training sample, raw
At the quick identification model of cholelithiasis CT medical image；
4) detection components, using the cholelithiasis CT medical image verifying collection newly constructed, to trained cholelithiasis CT medical treatment
The quick identification model of image is verified.
A kind of cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks, basic step are as follows；
1) cholelithiasis CT medical image training set is constructed；
2) training set is handled, training sample required for generating；
3) the cholelithiasis CT medical image recognition training based on deep learning is carried out using training sample, generates cholelithiasis CT
The quick identification model of medical image；
4) it constructs new cholelithiasis CT medical image and verifies collection；
5) the trained quick identification model of cholelithiasis CT medical image is verified.
A kind of diagnosis process of the cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks is one
The process of a continuous training study, the convolutional neural networks structure of cholelithiasis intelligent auxiliary diagnosis algorithm therein includes that data are pre-
Processing stage, local convolution stage and global convolution stage, the data preprocessing phase includes to cholelithiasis CT medical image
The data enhancement operations such as rotation, scaling and translation；The local convolution stage includes the convolution sum pond of YOLO convolutional neural networks
The operation such as change, the label of the focal area of contrast enhancement processing and cholelithiasis CT medical image to cholelithiasis CT medical image
Operation；The global convolution stage include multidimensional convolution and pondization operation, to the cholelithiasis CT medical image comprising marked region into
Then row feature extraction carries out last identifying processing in the full articulamentum for being transferred to network last layer.
A kind of cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks, first has to cholelithiasis
Image in CT medical image training set is analyzed, include the following steps in any one or it is multiple:
1) the step of image in the training set being zoomed in or out；
2) spin step is carried out to the image in the training set；
3) translation step is carried out to the image in the training set；
4) the step of radiation transformation being carried out to the image in the training set；
5) the step of image degree of comparing in the training set being enhanced；
6) the step of focal area label being carried out to the image in the training set.
Method of the invention can be introduced into major mainstream hospital, and the equipment for combining major mainstream hospital by we, into one
Step improves into a whole set of cholelithiasis CT medical image intelligent diagnostics process, and comprehensive various big hospital is worked throughout the year in the liver of a line
The medical knowledge of many years of gallbladder section domain expert constantly removes the cholelithiasis CT medical data collection for expanding us, mentions for the present invention
The data source that height is constantly updated, optimizes the iterative process of this model training and verifying, not to the parameter of deep learning network model
Disconnected tuning and update, to greatly improve the present invention to the rate of correct diagnosis of cholelithiasis CT medical image.By to magnanimity disease
The study of example simultaneously excavates important information therein and disease inherent law, it is established that a set of perfect cholelithiasis feature conclude and
Diagnosing model, the final artificial intelligence auxiliary diagnosis realized to cholelithiasis.
The above is only preferred embodiment of the invention, not does limitation in any form to the present invention.
It is any to be familiar with this profession although then the present invention is not intended to limit the invention with preferred embodiment is disclosed above
Technical staff, without departing from the scope of the present invention, when can use above-mentioned technology contents be modified or
The embodiment for being modified to equivalent variations, but but without departing from the technical solutions of the present invention, technology according to the present invention is real
Any simple modification, equivalent change and modification that the above implementation content of verifying is done, still fall within the model of technical solution of the present invention
In enclosing.
Claims (4)
1. a kind of cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks characterized by comprising
1) cholelithiasis CT medical image training set is constructed；
2) training set is handled, training sample required for generating；
3) the cholelithiasis CT medical image recognition training based on deep learning is carried out using training sample, generates cholelithiasis CT medical treatment
The quick identification model of image；
4) it constructs new cholelithiasis CT medical image and verifies collection；
5) the trained quick identification model of cholelithiasis CT medical image is verified.
2. a kind of quick side of identification of cholelithiasis CT medical image based on YOLO convolutional neural networks according to claim 1
Method, which is characterized in that the image in the cholelithiasis CT medical image training set is analyzed, is further comprised the steps
In any one or it is multiple:
1) the step of image in the training set being zoomed in or out；
2) spin step is carried out to the image in the training set；
3) translation step is carried out to the image in the training set；
4) the step of radiation transformation being carried out to the image in the training set；
5) the step of image degree of comparing in the training set being enhanced；
6) the step of focal area label being carried out to the image in the training set.
3. a kind of quick side of identification of cholelithiasis CT medical image based on YOLO convolutional neural networks according to claim 1
Method, which is characterized in that convolutional neural networks structure includes:
1) data preprocessing phase, including zooming in or out, rotating, translating and radiating variation to cholelithiasis CT medical image
And etc., the pretreatment of complete paired data；
2) it in the local convolution stage, is operated including one-dimensional convolution sum pond etc., utilizes the fireballing spy of YOLO convolutional neural networks
Point, the marking operation of the focal area of contrast enhancement processing and cholelithiasis CT medical image to cholelithiasis CT medical image；
3) the global convolution stage, convolution and pondization including YOLO convolutional neural networks operate, to the cholelith comprising marked region
Sick CT medical image carries out feature extraction, then carries out last identifying processing in the full articulamentum for being transferred to network last layer.
4. a kind of quick side of identification of cholelithiasis CT medical image based on YOLO convolutional neural networks according to claim 1
Method, it is characterised in that comprise the following modules:
1) input module, for inputting cholelithiasis CT medical image data sets；
2) analytic unit generates multiple training samples for handling the cholelithiasis CT medical image；
3) training assembly carries out the cholelithiasis CT medical image recognition training based on deep learning using training sample, generates gallbladder
The quick identification model of stone disease CT medical image；
4) detection components, using the cholelithiasis CT medical image verifying collection newly constructed, to trained cholelithiasis CT medical image
Quick identification model is verified.
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| PCT/CN2019/128109 WO2020168820A1 (en) | 2019-02-22 | 2019-12-25 | Yolo convolutional neural network-based cholelithiasis ct medical image data enhancement method |
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| CN112419348B (en) * | 2020-11-18 | 2024-02-09 | 西安电子科技大学 | Male pelvic cavity CT segmentation method based on multitask learning edge correction network |
| CN113256579A (en) * | 2021-05-19 | 2021-08-13 | 扬州大学 | Pulmonary tuberculosis recognition system based on pre-training model |
| CN113657492A (en) * | 2021-08-17 | 2021-11-16 | 上海海事大学 | Classification method of pen petrochemical stone images |
| CN114048806A (en) * | 2021-11-09 | 2022-02-15 | 安徽大学 | Alzheimer disease auxiliary diagnosis model classification method based on fine-grained deep learning |
| CN114742917B (en) * | 2022-04-25 | 2024-04-26 | 桂林电子科技大学 | CT image segmentation method based on convolutional neural network |
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