Deep learning algorithm-based classification method of bacterial pneumonia and viral pneumonia in children

A technology of deep learning and classification methods, applied in computing, image analysis, computer components and other directions, it can solve problems such as different images, human bones obscuring human organs, etc., to achieve the effect of reducing the amount of calculation

Active Publication Date: 2018-06-15
SUN YAT SEN UNIV
View PDF3 Cites 37 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there are still serious noises in chest X-ray images, such as occlusion of human bones, interference of bronchi and blood vessels, human organs, etc., and images vary with i

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
  • Deep learning algorithm-based classification method of bacterial pneumonia and viral pneumonia in children
  • Deep learning algorithm-based classification method of bacterial pneumonia and viral pneumonia in children
  • Deep learning algorithm-based classification method of bacterial pneumonia and viral pneumonia in children

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0063] Embodiment 1: The specific steps of the classification method of bacterial and viral pneumonia in children based on deep learning algorithm of the present invention

[0064] Such as figure 1 As shown, the classification method of bacterial and viral children's pneumonia based on the deep learning algorithm of the present invention includes (1) in the preprocessing stage, using the full convolutional network semantic segmentation model to carry out transfer learning to segment the lungs from the chest X-ray image region as the region of interest; (2) input the extracted region of interest into the convolutional neural network model to train the classifier to predict the category of the unknown chest X-ray image; (3) use the trained convolutional neural network model to extract The high-dimensional features of the region of interest, while using traditional image processing methods to extract the low-dimensional features of the region of interest, the high and low-dimens...

Embodiment 2

[0089] Embodiment 2: The recognition effect experiment of the classification method of bacterial and viral pneumonia in children based on the deep learning algorithm of the present invention

[0090] 1. Experimental data set: including JSRT public data set (a total of 247 images and segmented lung mask images), Montgomery public data set (a total of 138 images and segmented lung mask images) and women in Guangzhou Children's Hospital dataset (a total of 568 images are divided into two categories: bacterial and viral pneumonia);

[0091] 2. Experimental environment: Matlab 2016a platform, Caffe framework and Python;

[0092] 3. Experimental tool set: full convolutional network model trained by PASCAL VOC2012 dataset, AlexNet convolutional neural network model trained by ImageNet, Anaconda python library;

[0093] 4. Experimental method: The above-mentioned 385 images of JSRT and Montgomery and the lung mask image were divided into a training set and a verification set accordin...

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 deep learning algorithm-based classification method of bacterial pneumonia and viral pneumonia in children. According to the method, a source data set is manually labeled; onthe basis of the combination of a full convolutional network semantic segmentation algorithm and a convolutional neural network algorithm, the full convolutional network semantic segmentation algorithm is adopted to perform lung region foreground segmentation on an image so as to obtain a region of interest, the extracted region of interest is inputted to a convolutional neural network model so asto train a classifier, and therefore, the category of an unknown chest X-ray image can be predicted, and the high-dimensional features of the region of interest are extracted; and a traditional imageprocessing method is adopted to extract the low-dimensional features of the region of interest; and the high-dimensional features and the low-dimensional features are used to train a non-linear classifier; and the category of the unknown X-ray image is predicted, and the type of the pneumonia of a patient can be judged. Since a main component analysis algorithm is used to perform dimensionality reduction on the features, and therefore, the amount of calculation can be reduced; and the features which have been subjected to mixed dimensionality reduction are inputted into the nonlinear classifier, and the category of the unknown X-ray image can be predicted.

Description

technical field [0001] The present invention relates to the fields of computer vision technology and medical image processing, and more specifically, to a method for classifying bacterial and viral pneumonia in children based on deep learning algorithms. Background technique [0002] Pneumonia is a common and frequently-occurring disease in children, and it is also the leading cause of death in children. Pneumonia in children is mostly caused by bacteria and viruses, and a few are caused by mycoplasma and fungi. Pathogen diagnosis is an important basis for clinically correct antibiotic selection. Chest X-ray is one of the most common aids in the diagnosis of pneumonia. With the rapid development of the computer field and the success of deep learning algorithms, it has gradually set off an upsurge in the field of computer-aided diagnosis, and a large number of image processing algorithms and disease predictions based on X-ray images have emerged. Classification algorithm, th...

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): G06K9/32G06K9/62G06N3/04G06T7/11
CPCG06T7/11G06T2207/30061G06V10/25G06N3/045G06F18/213G06F18/2453
Inventor 辜祥宏杨然
Owner SUN YAT SEN UNIV
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