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

Skin disease detecting method based on deep learning and system thereof

A skin disease and deep learning technology, applied in the field of skin disease detection system based on deep learning, can solve problems such as poor professionalism and poor consultation effect, and achieve the effect of improving detection effect and performance

Active Publication Date: 2018-04-20
深圳市宜远智能科技有限公司
View PDF5 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the current shortage of dermatologists, there are fewer dermatologists with expertise in facial skin diagnosis
Moreover, in many cases, patients do not take the initiative to seek a doctor's diagnosis, but consult practitioners in the beauty industry. Due to the poor professionalism of such personnel, the consultation effect is often not good

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
  • Skin disease detecting method based on deep learning and system thereof
  • Skin disease detecting method based on deep learning and system thereof
  • Skin disease detecting method based on deep learning and system thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] reference figure 1 , The first embodiment discloses a skin disease detection method based on deep learning, the method includes:

[0040] S100. Before the neural network model performs skin detection, use pictures marked with disease areas and disease types of skin diseases as training data, and train an improved Faster RCNN neural network model after preprocessing;

[0041] Among them, the neural network model uses a deep-width residual network to perform feature extraction on the input image, and the extracted feature image is used as a shared feature of the disease area and the disease type to achieve two optimization goals while learning.

[0042] S200. When performing skin detection, preprocess the picture of the skin to be detected and input it into an improved Faster RCNN neural network model, which outputs the disease area and disease type of the skin to be detected.

[0043] reference figure 2 Specifically, the neural network model in this embodiment includes:

[0044] ...

Embodiment 2

[0054] Based on the same inventive concept, the present invention also discloses a skin disease detection system based on deep learning. The system disclosed in the second embodiment includes: a picture input layer and an improved Faster RCNN neural network model. Among them, the picture input layer is used to preprocess the input picture and input it into the neural network model, and the neural network model is used to output and detect the disease area and disease type of the skin.

[0055] It should be noted that before the neural network model performs skin detection, pictures with the disease area and disease type labeled with skin diseases can be used as training data, and the neural network model is trained after preprocessing, and the neural network model after training is completed. The network model is ready for use.

[0056] In this embodiment, the neural network model uses a depth-width residual network to perform feature extraction on input pictures, and the extracted...

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 discloses a skin disease detecting method based on deep learning and a system thereof. The method comprises the steps of preprocessing a picture of a to-be-detected skin, inputting the picture into an improved Faster RCNN neural network model, wherein the neural network model outputs the disease area and the disease type of the to-be-detected skin; wherein the neural network model utilizes a deep width residual error network for performing characteristic extraction on the input picture, and using the extracted characteristic picture as the shared characteristic of the disease area and the disease type for realizing simultaneous learning of two optimization objects. The skin disease detecting method can more effectively improve model performance and has improved detecting effect than basic Faster RCNN. The method and the system are suitable for face skin disease detection and disease detection at other parts or non-healthy area detection. The method and the system can be used for detection of various diseases in a medical industry and a medical beautifying industry.

Description

Technical field [0001] The invention relates to the field of skin disease detection, in particular to a skin disease detection system based on deep learning. Background technique [0002] In recent years, the incidence of facial skin diseases is high and on the rise, especially those closely related to skin care products, such as acne, sensitive facial skin, hormone-dependent dermatitis, perioral dermatitis, rosacea, chloasma, etc. are all common , And there are many types. How to choose skin care products and how to coordinate the simultaneous use of skin care products and topical drugs are issues that people, especially women, are particularly concerned about. The prerequisite for choosing the right skin care products is to make accurate judgments on facial skin diseases, otherwise it will aggravate the condition due to improper use of skin care products. Due to the current shortage of dermatologists, there are fewer dermatologists with expertise in facial skin diagnosis. Mo...

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): G06T7/00G06N3/04G06N3/08
CPCG06N3/08G06T7/0012G06T2207/20081G06T2207/30088G06N3/045
Inventor 刘凯吴志力刘晓明
Owner 深圳市宜远智能科技有限公司
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