CN109215021A - A kind of cholelithiasis CT medical image method for quickly identifying based on deep learning - Google Patents
A kind of cholelithiasis CT medical image method for quickly identifying based on deep learning 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 deep learning, designed image processing, medical big data, deep learning field.Include: 1) to acquire cholelithiasis CT medical image, constructs training set；2) image in 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 the trained quick identification model of cholelithiasis CT medical image；4) new cholelithiasis CT 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 cholelithiasis CT 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 deep learning, belongs to artificial intelligence
Field
Background technique
Cholelithiasis is a kind of common disease of digestive system, and disease is various, and pathogenic factors is intricate, has disease incidence
The features such as height, molten row's stone are difficult.In addition, the type and form of cholelithiasis are varied, the lesion form of part cholelithiasis
Be it is closely similar, this hinders the correct diagnosis and treatment of cholelithiasis significantly.In this case, the hepatology doctor of some youths needs
Prolonged study skillfully can grasp the technical ability for diagnosing cholelithiasis, this brings huge to the clinical diagnosis of hepatology doctor
Big challenge.And for the patient, cholelithiasis cannot cannot such as be controlled in time according to itself illness in online access data
It treats, there is carcinogenic danger.
It is createed safe and efficient auxiliary by artificial intelligence deep learning in combination with cholelithiasis big data and knowledge etc.
The diagnostic method of doctor is helped, and then provides more effective help for the correct diagnosis and treatment of cholelithiasis, this is based on deep learning
Cholelithiasis intelligent auxiliary diagnosis method.
Cholelithiasis type and form are varied, so that needing very more cholelithiasis medical treatment during model training
Image data.Common practice is to have initially set up cholelithiasis CT medical image training set, in the method benefit using deep learning
A large amount of training set of images is trained with model, generates trained model, then constructs new image authentication collection to mould
Type is verified, and final recognition result is obtained.But the type of cholelithiasis and form are varied so that its clinical diagnosis
Process is extremely difficult, brings great challenge to the clinical diagnosis of cholelithiasis.When doctor is in the medical image of observation cholelithiasis
When, it needs to take a significant amount of time observation, check, determine the type, form and position of cholelithiasis.According to investigations, existing market
The deep learning model of the upper overwhelming majority can't complete above-mentioned task.
Summary of the invention
The purpose of the present invention is to solve difficulties present in above-mentioned disease in the liver and gallbladder field, provide a kind of based on depth
The cholelithiasis CT medical image method for quickly identifying of habit can help the doctor of cholelithiasis clinic to carry out correct diagnosis and treatment, improve
The accuracy rate of cholelithiasis diagnosis.
The present invention is to solve difficulty present in above-mentioned disease in the liver and gallbladder field, and used technical solution is: Yi Zhongji
In the cholelithiasis CT medical image method for quickly identifying of deep learning.
A kind of cholelithiasis CT medical image method for quickly identifying based on deep learning 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
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.
The further cholelithiasis CT medical image method for quickly identifying based on deep learning, convolutional neural networks structure
Include:
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., to the contrast of cholelithiasis CT medical image
The marking operation of the focal area of enhancing processing and cholelithiasis CT medical image；
3) global convolution stage, convolution and pondization including multidimensional operate, to the cholelithiasis CT medical treatment comprising marked region
Image carries out feature extraction, then carries out last identifying processing in the full articulamentum for being transferred to network last layer.
The further cholelithiasis CT medical image method for quickly identifying based on deep learning, it is characterised in that including following
Module:
1) input module, for inputting cholelithiasis CT medical image data sets；
2) data analysis module generates multiple training samples for handling the cholelithiasis CT medical image；
3) model training module carries out the identification instruction of the cholelithiasis CT medical image based on deep learning using training sample
Practice, generates the quick identification model of cholelithiasis CT medical image；
4) model checking module, using the cholelithiasis CT medical image verifying collection newly constructed, to trained cholelithiasis CT
The quick identification model of medical 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, a kind of cholelithiasis CT medical image based on the deep learning quickly side of identification is provided
Method realizes the feature to cholelithiasis CT medical image using technologies such as cholelithiasis CT medical image data and deep learnings
It automatically extracts and intelligent diagnostics, entire diagnosis process quickly, efficiently, improves the accuracy of cholelithiasis diagnosis.Based on depth
The cholelithiasis CT medical image method for quickly identifying of habit, type and the form for solving cholelithiasis are varied so that it is clinical
Difficult problem during diagnosis.Relative to traditional autonomous diagnostic method of doctor, the present invention can in a very short period of time, really
Determine the type, form and position of cholelithiasis, and provides diagnostic result.
Detailed description of the invention
Fig. 1 is the cholelithiasis CT medical image method for quickly identifying based on deep learning of embodiment according to the present invention
Flow diagram
Fig. 2 is that the training sample of embodiment according to the present invention generates schematic diagram
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, base
In the cholelithiasis CT medical image method for quickly identifying of deep learning, comprise the following modules:
1) input module, for inputting cholelithiasis CT medical image data sets；
2) data analysis module generates multiple training samples for handling the cholelithiasis CT medical image；
3) model training module carries out the identification instruction of the cholelithiasis CT medical image based on deep learning using training sample
Practice, generates the quick identification model of cholelithiasis CT medical image；
4) model checking module, using the cholelithiasis CT medical image verifying collection newly constructed, to trained cholelithiasis CT
The quick identification model of medical image is verified.
A kind of cholelithiasis CT medical image method for quickly identifying based on deep learning, 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.
The diagnosis process of cholelithiasis CT medical image method for quickly identifying based on deep learning is that a constantly training is learned
The process of habit, the convolutional neural networks structure of cholelithiasis intelligent auxiliary diagnosis algorithm therein include data preprocessing phase, office
Portion's convolution stage and global convolution stage, the data preprocessing phase includes the rotation to cholelithiasis CT medical image, scaling
And the data enhancement operations such as translation；The local convolution stage includes the operation such as one-dimensional convolution sum pond, to cholelithiasis CT medical treatment
The marking operation of the focal area of the contrast enhancement processing and cholelithiasis CT medical image of image；The global convolution stage includes more
The convolution and pondization of dimension operate, and carry out feature extraction to the cholelithiasis CT medical image comprising marked region, are then being transferred to
The full articulamentum of network last layer carries out last identifying processing.
Cholelithiasis CT medical image method for quickly identifying based on deep learning first has to instruct cholelithiasis CT medical image
Practice concentrate image 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 deep learning 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. the cholelithiasis CT medical image Fast Recognition Algorithm according to claim 1 based on deep learning, feature exist
In, the image in the cholelithiasis CT medical image training set is analyzed, further comprise the steps in it is any one
It is a or 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. the cholelithiasis CT medical image method for quickly identifying according to claim 1 based on deep learning, feature exist
In 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., the contrast of cholelithiasis CT medical image is enhanced
The marking operation of the focal area of processing and cholelithiasis CT medical image；
3) the global convolution stage, convolution and pondization including multidimensional operate, to the cholelithiasis CT medical image comprising marked region
Feature extraction is carried out, then carries out last identifying processing in the full articulamentum for being transferred to network last layer.
4. the cholelithiasis CT medical image method for quickly identifying according to claim 1 based on deep learning, feature exist
In comprising the following modules:
1) input module, for inputting cholelithiasis CT medical image data sets；
2) data analysis module generates multiple training samples for handling the cholelithiasis CT medical image；
3) model training module 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) model checking module, using the cholelithiasis CT medical image verifying collection newly constructed, to trained cholelithiasis CT medical treatment
The quick identification model of image is verified.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109902734A (en) * | 2019-02-22 | 2019-06-18 | 中国石油大学(华东) | A kind of cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks |
| CN110458246A (en) * | 2019-08-27 | 2019-11-15 | 上海理工大学 | A kind of Biliary Calculi classification method based on deep learning |
| CN110459300A (en) * | 2019-07-22 | 2019-11-15 | 中国石油大学(华东) | A kind of lung cancer pathology type diagnostic method based on computer vision and CT images |
| CN113705578A (en) * | 2021-09-10 | 2021-11-26 | 北京航空航天大学 | Bile duct form identification method and device |
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2018
- 2018-09-06 CN CN201811036666.7A patent/CN109215021A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109902734A (en) * | 2019-02-22 | 2019-06-18 | 中国石油大学(华东) | A kind of cholelithiasis CT medical image method for quickly identifying based on YOLO convolutional neural networks |
| WO2020168820A1 (en) * | 2019-02-22 | 2020-08-27 | 中国石油大学(华东) | Yolo convolutional neural network-based cholelithiasis ct medical image data enhancement method |
| CN110459300A (en) * | 2019-07-22 | 2019-11-15 | 中国石油大学(华东) | A kind of lung cancer pathology type diagnostic method based on computer vision and CT images |
| CN110458246A (en) * | 2019-08-27 | 2019-11-15 | 上海理工大学 | A kind of Biliary Calculi classification method based on deep learning |
| CN113705578A (en) * | 2021-09-10 | 2021-11-26 | 北京航空航天大学 | Bile duct form identification method and device |
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Application publication date: 20190115 |