Intestinal lesion auxiliary diagnosis method based on non-normalized depth residual error and attention mechanism

A technology for assisting diagnosis and attention, applied in the field of medical image processing, can solve the problems of blurred boundary walls of lesions, inadequate extraction of subtle features, and large differences in the size and shape of intra-class lesions, so as to overcome large differences in shape and improve classification performance. Effect

Pending Publication Date: 2021-08-13
ZHEJIANG UNIV OF TECH
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the problems that the existing CNN does not extract fine features of local lesion areas in place, the size and shape of lesions within a class differ greatly, the boundary between the lesion edge and the n

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
  • Intestinal lesion auxiliary diagnosis method based on non-normalized depth residual error and attention mechanism
  • Intestinal lesion auxiliary diagnosis method based on non-normalized depth residual error and attention mechanism
  • Intestinal lesion auxiliary diagnosis method based on non-normalized depth residual error and attention mechanism

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0030] The present invention will be further described below with reference to the accompanying drawings.

[0031] Refer Figure 1 ~ 5 A intestinal lesion auxiliary diagnostic method based on non-standardized depth residual and attention mechanism, including the following steps:

[0032] Step 1: Enter the image data set x = {x 1 , X 2 , ..., x n }, Where the X matrix represents the data set, n represents the total sample number, X i ∈R 224×224×3 Indicates feature vector composed of three channel pixel values ​​of input images, (X i Y i ) Represents sample I, Y i Indicates the sample category tag, its value is 0, which represents 1 means polyps, its value is 2 indicating ulcers, and after training a classification model, the characteristic vector X i For input, the result tag of the predicted output is 0, 1 or 2, so that it can judge whether it is normal, polyp or ulcer;

[0033] Step 2: Due to the size of the inner lesions in the intestinal data concentration, the shape change diff...

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

An intestinal lesion auxiliary diagnosis method based on a non-normalized depth residual error and attention mechanism comprises the steps of firstly dividing an original intestinal data set randomly into a training set, a verification set and a test set, and pre-processing; directly inputting the training set into a network, wherein the network is mainly composed of three parts, namely a feature extractor, an attention branch and a perception branch; performing end-to-end mode training on the network by combining the sum of the loss functions of the attention branch and the perception branch to generate an optimal model; and inputting a test set image to test the optimal model, and evaluating the performance of the optimal model by using three indexes, namely Accuracy, Sensitivity and Specificity. Compared with a classical ResNet network, the method of the present invention has a better effect, the classification performance is well improved, the method can be better applied to the auxiliary diagnosis of intestinal lesions, and the method has a better practical engineering application value.

Description

technical field [0001] The invention relates to the technical field of medical image processing, and uses intestinal endoscopic images for auxiliary diagnosis of intestinal lesions. Specifically, it relates to an intestinal image processing method based on unnormalized depth residual and attention mechanism. Background technique [0002] Intestinal polyps and ulcers are the main risk factors for early intestinal cancer. Screening for bowel precancerous lesions is important for early bowel cancer prevention. Intestinal endoscopy is the main method for screening and preventing cancer. Clinically, the accuracy of intestinal endoscopy is closely related to the doctor's experience, the operation is difficult, and it is easy to cause misdiagnosis or missed diagnosis. In order to improve the accuracy and effectiveness of intestinal endoscopy, researchers at home and abroad have proposed many methods for auxiliary diagnosis of intestinal lesions, mainly including: using image seg...

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
IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08G16H50/20
CPCG06T7/0012G06N3/08G16H50/20G06T2207/30028G06N3/045G06F18/2411G06F18/214
Inventor 李胜程珊何熊熊夏瑞瑞王栋超郝明杰
Owner ZHEJIANG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap