Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Chronic obstructive pulmonary disease detection system based on deep neural network

A deep neural network and chronic obstructive technology, applied in the field of neural network, can solve the problem of inaccurate detection results of COPD detection system, and achieve the effect of improving accuracy

Inactive Publication Date: 2018-04-20
北京医拍智能科技有限公司
View PDF5 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a detection system for chronic obstructive pulmonary disease based on a deep neural network, using a deep neural network to discover and distinguish early subtle pulmonary lobular lesions that are difficult to identify with the naked eye, and solve the COPD detection system in the prior art Technical problems with inaccurate test results

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Chronic obstructive pulmonary disease detection system based on deep neural network
  • Chronic obstructive pulmonary disease detection system based on deep neural network
  • Chronic obstructive pulmonary disease detection system based on deep neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] figure 1 It is a schematic diagram of a detection system for chronic obstructive pulmonary disease based on a deep neural network according to an embodiment of the present invention, such as figure 1 As shown, the embodiment of the present invention provides a COPD detection system based on a deep neural network, including a preprocessing module 10 and a detection module 20, wherein,

[0042] The preprocessing module 10 is configured to perform grayscale processing on the acquired chest CT image of the first patient, and extract a plurality of lung lobular region images in the chest CT image after grayscale processing;

[0043] The detection module 20 is used to input the acquired body mass index (BMI) of the first patient and the images of the plurality of pulmonary lobular regions into the trained deep neural network model to obtain the information of the first patient suffering from chronic obstructive pulmonary disease. probability value.

[0044] Further, the dee...

Embodiment 2

[0077] figure 2 A schematic structural diagram of an electronic device for COPD detection provided by an embodiment of the present invention, such as figure 2 As shown, the device includes: a processor (processor) 801, a memory (memory) 802 and a bus 803;

[0078] Wherein, the processor 801 and the memory 802 complete mutual communication through the bus 803;

[0079] The processor 801 is used to call the program instructions in the memory 802 to perform the following steps:

[0080] performing grayscale processing on the acquired chest CT image of the first patient, and extracting images of a plurality of pulmonary lobular regions in the chest CT image after grayscale processing;

[0081] Inputting the obtained BMI of the first patient and the images of the plurality of pulmonary lobular regions into the trained deep neural network model to obtain the probability value of the first patient suffering from chronic obstructive pulmonary disease.

Embodiment 3

[0083] An embodiment of the present invention discloses a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, The computer is capable of performing the following steps:

[0084] performing grayscale processing on the acquired chest CT image of the first patient, and extracting images of a plurality of pulmonary lobular regions in the chest CT image after grayscale processing;

[0085] Inputting the obtained BMI of the first patient and the images of the plurality of pulmonary lobular regions into the trained deep neural network model to obtain the probability value of the first patient suffering from chronic obstructive pulmonary disease.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a chronic obstructive pulmonary disease detection system based on a deep neural network. The system comprises a preprocessing module, which is used for carrying out gray-scale treatment on an obtained chest CT image of a first patient and extracting a plurality of pulmonary lobule area images of the chest CT image obtained after gray-scale treatment; and a detection module,which is used for inputting obtained body mass index BMI of the first patient and the plurality of pulmonary lobule area images into a trained deep neural network model, and obtaining probability of the first patient in getting the chronic obstructive pulmonary disease. The chronic obstructive pulmonary disease detection system based on the deep neural network, through combination of the deep neural network and medical images, and with clinical experience knowledge obtained by diagnosing the COPD manually being as prior knowledge, carries out detection on early-stage pulmonary lobule small lesions, and carries out highly-reliable prediction on the cases, thereby improving COPD detection accuracy.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a detection system for chronic obstructive pulmonary disease based on a deep neural network. Background technique [0002] With the rapid development and wide application of computer technology, computer-aided diagnosis plays an increasingly important role in human health. [0003] In the prior art, the detection method of the detection system of chronic obstructive pulmonary disease (chronic obstructivepulmonary disease, COPD) by the computer-aided diagnosis method is as follows: first, obtain the patient's electronic computed tomography (Computed Tomography, CT) image, then through the Image processing is performed on the CT image, and then according to the pixel value of each pixel of the CT image, the relationship between the pixel and the preset pixel threshold is judged to determine whether the pixel is an image pixel of the lesion area, and finally according to the...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 夏一帆杨琼吴诗展
Owner 北京医拍智能科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com