CN108229584A - A kind of Multimodal medical image recognition methods and device based on deep learning - Google Patents
A kind of Multimodal medical image recognition methods and device based on deep learning Download PDFInfo
- Publication number
- CN108229584A CN108229584A CN201810108516.6A CN201810108516A CN108229584A CN 108229584 A CN108229584 A CN 108229584A CN 201810108516 A CN201810108516 A CN 201810108516A CN 108229584 A CN108229584 A CN 108229584A
- Authority
- CN
- China
- Prior art keywords
- medical image
- multimodal medical
- module
- lesion
- multimodal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/285—Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention discloses a kind of Multimodal medical image recognition methods based on deep learning and devices, include Multimodal medical image acquisition module, Multimodal medical image display module, Multimodal medical image detection module and Multimodal medical image identification module；The Multimodal medical image acquisition module, Multimodal medical image display module, Multimodal medical image detection module and Multimodal medical image identification module are sequentially connected；The present invention carries out the diseased region of patient by Multimodal medical image acquisition module, Multimodal medical image display module Pathological Information acquisition and the image procossing of lesion region, and pass through Multimodal medical image detection module and carry out Pathological Information analysis and detection, final lesion result is determined eventually by Multimodal medical image identification module automatic identification；Diagnosis efficiency is effectively improved, reduces the workload of medical staff.
Description
Technical field
The present invention relates to image identification technical field, specifically a kind of multi-modal medicine based on deep learning
Image recognition method and device.
Background technology
At present, with accurate medical treatment and the arriving in big data epoch, other than diagnosing text information, the analysis of image data
And application has become one of link of clinical medicine more core.
Medical image refers to for medical treatment or medical research, to human body or human body part, obtained with non-intruding mode in
The technology and processing procedure of tissue image of portion.It includes following two relatively independent research directions：Medical image system
(medical imaging system) and Medical Image Processing (medical image processing).The former refers to image
Row process, including to imaging mechanism, imaging device, imaging system analyze the problems such as research；The latter refers to having obtained
The image obtained further processes, the purpose is to make original inadequate clearly image restoration or for protrusion
Certain characteristic informations in image or pattern classification etc. is done to image.
Medical staff carries out manual identified to these medical images as needed, to be diagnosed to corresponding virus.Due to
The daily medical image quantity of hospital is thousands of, and workload is very big, and diagnosis efficiency is relatively low.In order to reduce the work of medical staff
Amount, is now badly in need of a kind of novel medical image recognition methods.
Invention content
In view of the defects and deficiencies of the prior art, the present invention intends to provide a kind of based on the multi-modal of deep learning
Medical image recognition methods and device.
To achieve the above object, the technical solution adopted by the present invention is：
A kind of Multimodal medical image recognition methods based on deep learning, comprises the concrete steps that：
1st, the Multimodal medical image identification device based on patient position to be detected, by the way that Multimodal medical image is acquired
The position checked required for module alignment patient, automatic collection and the lesion region information of identification patient；
2nd, Multimodal medical image display module carries out at image the patient's lesion region for acquiring and recognizing in step 1
Reason, the trunk information area block of automatic identification lesion region, and display and enhanced processing automatically；
3rd, Multimodal medical image detection module is to the lesion region in the Multimodal medical image display module in step 2
Lesion phenomenon, carry out targetedly lesion detection and lesion and analyze；
4th, the lesion detection in step 3 and lesion analysis result are passed through into Multimodal medical image identification module automatic identification
The final result of lesion.
A kind of Multimodal medical image identification device based on deep learning, comprising Multimodal medical image acquisition module,
Multimodal medical image display module, Multimodal medical image detection module and Multimodal medical image identification module；It is described
Multimodal medical image acquisition module, Multimodal medical image display module, Multimodal medical image detection module and multimode
State medical image identification module is sequentially connected；
Shown Multimodal medical image acquisition module, for the lesion region checked required for automatic collection and identification patient
Information；
Shown Multimodal medical image display module, for collecting lesion letter to Multimodal medical image acquisition module
Breath carries out image procossing, the trunk information area block of automatic identification lesion region, and display and enhanced processing automatically；
The Multimodal medical image detection module, for the lesion region in Multimodal medical image display module
Lesion phenomenon carries out targetedly lesion detection and is analyzed with lesion；
The Multimodal medical image identification module is tied for being detected to Multimodal medical image detection module with analysis
Fruit carries out the final result of automatic identification lesion.
As a further improvement on the present invention, the Multimodal medical image display module includes image processing module and figure
As amplification module.
As a further improvement on the present invention, the Multimodal medical image acquisition module is using ultrasonographic
Head.
With the above structure, beneficial effects of the present invention are：The present invention passes through Multimodal medical image acquisition module, more
Mode medical image display module carries out the diseased region of patient Pathological Information acquisition and the image procossing of lesion region, and leads to
It crosses Multimodal medical image detection module and carries out Pathological Information analysis and detection, eventually by Multimodal medical image identification module
Automatic identification determines final lesion result；The present invention effectively improves diagnosis efficiency, reduces the workload of medical staff.
Description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention, for those of ordinary skill in the art, without creative efforts, can be with
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the structural framing figure of the present invention.
Specific embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with attached drawing and specific implementation
Mode, the present invention will be described in further detail.It should be appreciated that the specific embodiments described herein are only explaining this
Invention, is not intended to limit the present invention.
Referring to shown in Fig. 1, present embodiment uses following technical scheme：
A kind of Multimodal medical image identification device based on deep learning, comprising Multimodal medical image acquisition module,
Multimodal medical image display module, Multimodal medical image detection module and Multimodal medical image identification module；It is described
Multimodal medical image acquisition module, Multimodal medical image display module, Multimodal medical image detection module and multimode
State medical image identification module is sequentially connected；
Shown Multimodal medical image acquisition module, for the lesion region checked required for automatic collection and identification patient
Information；The Multimodal medical image acquisition module is using ultrasonographic head.
Shown Multimodal medical image display module, for collecting lesion letter to Multimodal medical image acquisition module
Breath carries out image procossing, the trunk information area block of automatic identification lesion region, and display and enhanced processing automatically；The multimode
State medical image display module includes image processing module and image amplification module；Pass through image procossing in present embodiment
After module and image amplification module handle lesion image, increase the clarity of lesion image, convenient for quickly analyze with
Identification improves diagnostic accuracy.
The Multimodal medical image detection module, for the lesion region in Multimodal medical image display module
Lesion phenomenon carries out targetedly lesion detection and is analyzed with lesion；
The Multimodal medical image identification module is tied for being detected to Multimodal medical image detection module with analysis
Fruit carries out the final result of automatic identification lesion.
A kind of Multimodal medical image recognition methods based on deep learning, comprises the concrete steps that：
1st, the Multimodal medical image identification device based on patient position to be detected, by the way that Multimodal medical image is acquired
The position checked required for module alignment patient, automatic collection and the lesion region information of identification patient；
2nd, Multimodal medical image display module carries out at image the patient's lesion region for acquiring and recognizing in step 1
Reason, the trunk information area block of automatic identification lesion region, and display and enhanced processing automatically；
3rd, Multimodal medical image detection module is to the lesion region in the Multimodal medical image display module in step 2
Lesion phenomenon, carry out targetedly lesion detection and lesion and analyze；
4th, the lesion detection in step 3 and lesion analysis result are passed through into Multimodal medical image identification module automatic identification
The final result of lesion.
It is obvious to a person skilled in the art that the present invention is not limited to the details of above-mentioned exemplary embodiment, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Profit requirement rather than above description limit, it is intended that all by what is fallen within the meaning and scope of the equivalent requirements of the claims
Variation is included within the present invention.Any reference numeral in claim should not be considered as to the involved claim of limitation.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in each embodiment can also be properly combined, forms those skilled in the art
The other embodiment being appreciated that.
Claims (4)
1. a kind of Multimodal medical image recognition methods based on deep learning, it is characterised in that comprise the concrete steps that：
(1), the Multimodal medical image identification device based on patient position to be detected, by the way that Multimodal medical image is acquired mould
The position checked required for block alignment patient, automatic collection and the lesion region information of identification patient；
(2), Multimodal medical image display module carries out at image acquisition in step (1) with the patient's lesion region recognized
Reason, the trunk information area block of automatic identification lesion region, and display and enhanced processing automatically；
(3), Multimodal medical image detection module is to the lesion region in the Multimodal medical image display module in step (2)
Lesion phenomenon, carry out targetedly lesion detection and lesion and analyze；
(4), the lesion detection in step (3) and lesion analysis result are passed through into Multimodal medical image identification module automatic identification
The final result of lesion.
2. a kind of Multimodal medical image identification device based on deep learning according to claim 1, it is characterised in that：
Comprising Multimodal medical image acquisition module, Multimodal medical image display module, Multimodal medical image detection module and
Multimodal medical image identification module；The Multimodal medical image acquisition module, Multimodal medical image display module, multimode
State medical image detection module and Multimodal medical image identification module are sequentially connected；
Shown Multimodal medical image acquisition module, the lesion region for being checked required for automatic collection and identification patient are believed
Breath；
Shown Multimodal medical image display module, for Multimodal medical image acquisition module is collected Pathological Information into
Row image procossing, the trunk information area block of automatic identification lesion region, and display and enhanced processing automatically；
The Multimodal medical image detection module, for the lesion to the lesion region in Multimodal medical image display module
Phenomenon carries out targetedly lesion detection and is analyzed with lesion；
The Multimodal medical image identification module, for Multimodal medical image detection module is detected with analysis result into
The final result of row automatic identification lesion.
3. a kind of Multimodal medical image identification device based on deep learning according to claim 2, it is characterised in that：
The Multimodal medical image display module includes image processing module and image amplification module.
4. a kind of Multimodal medical image identification device based on deep learning according to claim 2, it is characterised in that：
The Multimodal medical image acquisition module is using ultrasonographic head.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810108516.6A CN108229584A (en) | 2018-02-02 | 2018-02-02 | A kind of Multimodal medical image recognition methods and device based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810108516.6A CN108229584A (en) | 2018-02-02 | 2018-02-02 | A kind of Multimodal medical image recognition methods and device based on deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108229584A true CN108229584A (en) | 2018-06-29 |
Family
ID=62670461
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810108516.6A Pending CN108229584A (en) | 2018-02-02 | 2018-02-02 | A kind of Multimodal medical image recognition methods and device based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108229584A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109346143A (en) * | 2018-12-07 | 2019-02-15 | 蔡荣杰 | A method of it improving ultrasonic examination and reports editing speed |
| CN109473168A (en) * | 2018-10-09 | 2019-03-15 | 五邑大学 | A kind of medical image robot and its control, medical image recognition methods |
| CN109493935A (en) * | 2018-12-21 | 2019-03-19 | 宜宝科技（北京）有限公司 | Inspection report management method and device based on hospital end |
| CN109754848A (en) * | 2018-12-21 | 2019-05-14 | 宜宝科技（北京）有限公司 | Approaches to IM and device based on medical care end |
| CN109785926A (en) * | 2018-12-21 | 2019-05-21 | 宜宝科技（北京）有限公司 | Inspection report processing method and processing device for unit end |
| CN110321946A (en) * | 2019-06-27 | 2019-10-11 | 郑州大学第一附属医院 | A kind of Multimodal medical image recognition methods and device based on deep learning |
| CN111415727A (en) * | 2020-03-13 | 2020-07-14 | 广州医科大学附属肿瘤医院 | Multi-mode image data management system and analysis method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070133852A1 (en) * | 2005-11-23 | 2007-06-14 | Jeffrey Collins | Method and system of computer-aided quantitative and qualitative analysis of medical images |
| CN103345576A (en) * | 2013-06-25 | 2013-10-09 | 上海交通大学 | Clinical history database diagnostic system based on four-modal medical image |
| CN106909778A (en) * | 2017-02-09 | 2017-06-30 | 北京市计算中心 | A kind of Multimodal medical image recognition methods and device based on deep learning |
| CN107153756A (en) * | 2016-03-04 | 2017-09-12 | 申强 | A kind of medical imaging diagnosis device |
| CN107220473A (en) * | 2016-03-21 | 2017-09-29 | 安丽华 | A kind of medical image processing system |
-
2018
- 2018-02-02 CN CN201810108516.6A patent/CN108229584A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070133852A1 (en) * | 2005-11-23 | 2007-06-14 | Jeffrey Collins | Method and system of computer-aided quantitative and qualitative analysis of medical images |
| CN103345576A (en) * | 2013-06-25 | 2013-10-09 | 上海交通大学 | Clinical history database diagnostic system based on four-modal medical image |
| CN107153756A (en) * | 2016-03-04 | 2017-09-12 | 申强 | A kind of medical imaging diagnosis device |
| CN107220473A (en) * | 2016-03-21 | 2017-09-29 | 安丽华 | A kind of medical image processing system |
| CN106909778A (en) * | 2017-02-09 | 2017-06-30 | 北京市计算中心 | A kind of Multimodal medical image recognition methods and device based on deep learning |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109473168A (en) * | 2018-10-09 | 2019-03-15 | 五邑大学 | A kind of medical image robot and its control, medical image recognition methods |
| CN109346143A (en) * | 2018-12-07 | 2019-02-15 | 蔡荣杰 | A method of it improving ultrasonic examination and reports editing speed |
| CN109346143B (en) * | 2018-12-07 | 2021-05-11 | 蔡荣杰 | Method for improving editing speed of ultrasonic examination report |
| CN109493935A (en) * | 2018-12-21 | 2019-03-19 | 宜宝科技（北京）有限公司 | Inspection report management method and device based on hospital end |
| CN109754848A (en) * | 2018-12-21 | 2019-05-14 | 宜宝科技（北京）有限公司 | Approaches to IM and device based on medical care end |
| CN109785926A (en) * | 2018-12-21 | 2019-05-21 | 宜宝科技（北京）有限公司 | Inspection report processing method and processing device for unit end |
| CN109493935B (en) * | 2018-12-21 | 2021-12-24 | 宜宝科技（北京）有限公司 | Examination and report management method and device based on hospital side |
| CN110321946A (en) * | 2019-06-27 | 2019-10-11 | 郑州大学第一附属医院 | A kind of Multimodal medical image recognition methods and device based on deep learning |
| CN111415727A (en) * | 2020-03-13 | 2020-07-14 | 广州医科大学附属肿瘤医院 | Multi-mode image data management system and analysis method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108229584A (en) | A kind of Multimodal medical image recognition methods and device based on deep learning | |
| CN109859203B (en) | Defect tooth image identification method based on deep learning | |
| CN110634125B (en) | Deep learning-based fetal ultrasound image identification method and system | |
| CN109948719B (en) | Automatic fundus image quality classification method based on residual dense module network structure | |
| WO2019052063A1 (en) | Medical image classification processing system and method based on artificial intelligence | |
| WO2021004174A1 (en) | Ultrasound image-based automatic measuring method and device for intrapartum cephalopelvic relationship | |
| CN101309645B (en) | Ultrasonic diagnosis device | |
| CN106909778A (en) | A kind of Multimodal medical image recognition methods and device based on deep learning | |
| CN104224129B (en) | A kind of vein blood vessel depth recognition method and prompt system | |
| CN110338759B (en) | Facial pain expression data acquisition method | |
| CN108806776A (en) | A method of the Multimodal medical image based on deep learning | |
| CN112927187A (en) | Method for automatically identifying and positioning focal cortical dysplasia epileptic focus | |
| CN111222464B (en) | Emotion analysis method and system | |
| CN109949272B (en) | Data acquisition method and system for identifying skin disease types and acquiring human skin pictures | |
| CN110070125A (en) | A kind of liver and gall surgical department's therapeutic scheme screening technique and system based on big data analysis | |
| CN114220543A (en) | Body and mind pain index evaluation method and system for tumor patient | |
| CN113298773A (en) | Heart view identification and left ventricle detection device and system based on deep learning | |
| CN109493340A (en) | Esophagus fundus ventricularis varication assistant diagnosis system and method under a kind of gastroscope | |
| CN117011892A (en) | Ultrasonic image automatic analysis and diagnosis system based on deep learning | |
| KR102242114B1 (en) | Oct medical image based artificial intelligence computer aided diagnosis system and its method | |
| CN113240799B (en) | Tooth three-dimensional model construction system based on medical big data | |
| CN114176602B (en) | Method for simultaneously positioning electrocardiograph P wave, QRS wave and T wave based on deep learning multi-target detection | |
| CN113197551B (en) | Multimode physiological nerve signal detection and experimental stimulation time alignment method | |
| CN205814348U (en) | Cardiac valve cardiechema signals mechanical vibration intelligent analysis system | |
| CN205597896U (en) | Traditional chinese medical science diagnosis state of an illness collection system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180629 |