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

Two-stage deep transfer learning traditional Chinese medicine tongue diagnosis model

A transfer learning and phase-based technology, applied in medical automated diagnosis, biological neural network models, informatics, etc., can solve problems such as random distribution of tongue images, affecting the accuracy of tongue image-assisted diagnosis and treatment, and unbalanced labels, so as to reduce training costs , Efficient feature extraction learning and the effect of fusion calculation

Active Publication Date: 2020-06-02
DALIAN UNIV OF TECH
View PDF5 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, the tongue diagnosis images are collected from daily diagnosis and treatment, the distribution of tongue images is random, and the labels are not balanced
These factors will affect the accuracy of tongue image-assisted diagnosis and treatment

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
  • Two-stage deep transfer learning traditional Chinese medicine tongue diagnosis model
  • Two-stage deep transfer learning traditional Chinese medicine tongue diagnosis model
  • Two-stage deep transfer learning traditional Chinese medicine tongue diagnosis model

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach

[0136] 2. Different from the traditional method, the present invention innovatively adopts the framework based on two-stage deep transfer learning for the first time. In the field of computer vision for object recognition, two important theories have been proven: first, deeper features have a stronger abstraction ability for input images; second, high-level visual features can be derived from low-level features (points, line, surface, shadow, light and shade, etc.). Although high-level features often have different morphological structures and feature representations in different tasks, the low-level features that make up these features can often be shared in different models. But training a deeper network also means greater difficulties: (1) It is more difficult to effectively transfer the calculation results to the deep layer of the network to avoid problems such as gradient dispersion or gradient disappearance. (2) Even if the model can abstract low-level features very wel...

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

A two-stage deep transfer learning traditional Chinese medicine tongue diagnosis model belongs to the technical field of traditional Chinese medicine auxiliary diagnosis and treatment. The method comprises the following steps: firstly, constructing a deep network based on a deep convolutional feature paradigm, fusing multi-scale features by using a pyramid strategy, and constructing deep abstractrepresentation of an input tongue image; then, designing two-stage deep transfer learning and obtaining the recognition capability of representative focus features in tongue image diagnosis in a targeted mode, thereby effectively solving the problem of data shortage, and reducing the training cost. On the basis, a focus examination cost function is designed, a deep migration model is trained, detection is carried out from different scales, an abnormal tongue image focus is marked, and the detection precision is improved. Finally, according to the examination result of the deep migration model,the process of combined use of multiple diagnostic methods of traditional Chinese medicine diagnosis and treatment is simulated, and real-time discrimination on abnormal tongue images is carried out,thereby improving the accuracy of diagnosis. The model designed by the invention can simulate the traditional Chinese medicine diagnosis theory, diagnose abnormal tongue images in real time, and provide clinical assistance and diagnosis and treatment suggestions for traditional Chinese medicine.

Description

technical field [0001] The invention belongs to the technical field of auxiliary diagnosis and treatment of traditional Chinese medicine, and relates to a two-stage deep transfer learning TCM tongue diagnosis model, which solves the problems of lack of data and low diagnostic accuracy faced by deep learning in the process of computerization of traditional Chinese medicine diagnosis and treatment methods. Background technique [0002] Traditional Chinese medicine is an important intangible cultural heritage of our country, which plays an important role in the treatment of chronic diseases and sudden diseases. With the common development of medicine and computer science, the modernization of traditional Chinese medicine has been paid more and more attention, and the standardization and computerization of traditional Chinese medicine diagnosis and treatment methods are imminent. It is of great practical significance to use advanced computer technology to simulate and reproduce ...

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): G16H20/90G16H50/20G06K9/00G06K9/62G06N3/04
CPCG16H20/90G16H50/20G06V40/10G06N3/045G06F18/253G06F18/214Y02A90/10
Inventor 陈志奎张旭高静李朋
Owner DALIAN UNIV OF TECH
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