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Lung disease classification method and device, and equipment

A lung disease and classification method technology, applied in the field of artificial intelligence, to avoid diagnostic bias and reduce work stress

Pending Publication Date: 2020-09-15
北京小白世纪网络科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This is a huge challenge for computers to automatically identify

Method used

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  • Lung disease classification method and device, and equipment
  • Lung disease classification method and device, and equipment
  • Lung disease classification method and device, and equipment

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0083] An embodiment of the present invention provides a lung disease classification device based on a deep learning model and X-ray images, such as Figure 4 As shown, it includes: a memory 40, a processor 42, and a computer program stored on the memory 40 and operable on the processor 42. When the computer program is executed by the processor 42, the following method steps are implemented:

[0084] Step 101, extract the mask image of the chest area in the X-ray image, input the mask image to the convolutional neural network of the pre-trained deep learning model to encode and extract the position feature information, and obtain the first feature vector x mask ; In step 101, extracting the chest area mask map in the X-ray image specifically includes:

[0085] Perform Gaussian blur processing on the X-ray image, use the Otsu algorithm to obtain the threshold value of the blurred X-ray image, and perform threshold segmentation on the X-ray image; use image morphology erosion op...

Embodiment 2

[0093] An embodiment of the present invention provides a computer-readable storage medium, where a program for realizing information transmission is stored on the computer-readable storage medium, and when the program is executed by the processor 42, the following method steps are implemented:

[0094] Step 101, extract the mask image of the chest area in the X-ray image, input the mask image to the convolutional neural network of the pre-trained deep learning model to encode and extract the position feature information, and obtain the first feature vector x mask ; In step 101, extracting the chest area mask map in the X-ray image specifically includes:

[0095] Perform Gaussian blur processing on the X-ray image, use the Otsu algorithm to obtain the threshold value of the blurred X-ray image, and perform threshold segmentation on the X-ray image; use image morphology erosion operation to process the X-ray image after threshold segmentation, so that the X-ray image The lung ar...

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Abstract

The invention discloses a lung disease classification method and device based on a deep learning model and an X-ray image, and equipment. The lung disease classification method comprises the followingsteps: extracting a chest region mask image in an X-ray image, and inputting the mask image into a convolutional neural network of a pre-trained deep learning model for coding to extract position feature information, thereby obtaining a first feature vector xmask; carrying out online data enhancement on the X-ray images, inputting the enhanced X-ray images into a DenSeNet network of a deep learning model to carry out feature extraction, and obtaining a second feature vector xdense for each X-ray image; and combining the first feature vector xmask with the second feature vector xdense to obtain a third feature vector xi = (xmask, xdense), and inputting the third feature vector xi into a full-connection network of a deep learning model to obtain a first classification result of the lung diseases.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a lung disease classification method, device and equipment based on a deep learning model and X-ray images. Background technique [0002] In the diagnosis of chest diseases, chest X-ray images are one of the most commonly used examination methods, and more than 2 billion people undergo chest X-ray examinations every year. This is critical for screening, diagnosis and treatment of chest disorders. [0003] But at the same time, the disease identification of chest X-ray images is a multi-label classification problem, and the same patient often produces multiple disease signs. This is a huge challenge for automatic computer recognition. Contents of the invention [0004] The object of the present invention is to provide a lung disease classification method, device and equipment based on a deep learning model and X-ray images, aiming to solve the above-men...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06N3/08G06T2207/10116G06T2207/20081G06T2207/20084G06T2207/30061G06N3/045G06F18/241Y02A90/10
Inventor 杜强高泽宾郭雨晨聂方兴张兴唐超
Owner 北京小白世纪网络科技有限公司