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Laryngeal disease diagnosis system based on deep learning neural network

A disease diagnosis and neural network technology, applied in the interdisciplinary field, can solve the problems of laryngoscopy image diagnosis efficiency and low diagnosis accuracy, and achieve the effect of improving the diagnosis efficiency and diagnosis accuracy, reducing the missed diagnosis and misdiagnosis rate, and improving the diagnosis.

Active Publication Date: 2020-08-04
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem of low diagnostic efficiency and diagnostic accuracy of laryngoscope images by traditional methods, and propose a laryngeal disease diagnosis system based on deep learning neural network

Method used

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  • Laryngeal disease diagnosis system based on deep learning neural network
  • Laryngeal disease diagnosis system based on deep learning neural network
  • Laryngeal disease diagnosis system based on deep learning neural network

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Experimental program
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specific Embodiment approach 1

[0020] Specific implementation mode 1. Combination figure 1 This embodiment will be described. A laryngeal disease diagnosis system based on a deep learning neural network described in this embodiment, the laryngeal disease diagnosis system includes an image acquisition main module, an image processing main module, a neural network main module, a training main module and a detection main module ;

[0021] The image collection main module is used to collect laryngoscope images, preprocess the collected laryngoscope images, obtain preprocessed images, and input the preprocessed images into the image processing main module;

[0022] The image processing main module is used to process the input image, and randomly divide the processed image into two groups of training sample set and verification sample set;

[0023] The neural network main module is used to build a network model for laryngeal disease diagnosis;

[0024] The training main module uses the training sample set to t...

specific Embodiment approach 2

[0031] Embodiment 2: The difference between this embodiment and Embodiment 1 is that the image acquisition main module scans the laryngoscope paper image output by the instrument or the laryngoscope paper image attached to the patient's medical record into an electronic The format of the image, after obtaining each complete laryngoscope electronic image, split the 4 sub-images on each image, and adjust the angle of the split image to make the split image correct;

[0032] After the white frame of the corrected image is removed, the image is adjusted to a uniform size; the size-adjusted image is input into the image processing main module.

specific Embodiment approach 3

[0033] Embodiment 3: The difference between this embodiment and Embodiment 1 is that the image processing main module is used to process the input image, and the specific process of processing is as follows:

[0034] Perform HSV decomposition on each image input to the image processing main module, wherein H, S and V represent the hue, saturation and brightness of the image respectively;

[0035] Do the following transformation on the points whose luminance value is greater than the luminance threshold l in the V channel (brightness channel):

[0036]

[0037] Among them, v represents the brightness value in the original channel, and l represents the brightness threshold (that is, when the brightness value v in the original channel is greater than the brightness threshold l, it will be transformed), v 1 is an intermediate variable, v 2 Represents the transformed brightness value;

[0038] Then normalize each image input to the image processing main module, so that the val...

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Abstract

The invention discloses a laryngeal disease diagnosis system based on a deep learning neural network, and belongs to the subject crossing field of combination of artificial intelligence and medical diagnosis. According to the invention, the problems of low diagnosis efficiency and low diagnosis accuracy of the laryngoscope image in the traditional method are solved. The laryngeal disease diagnosisnetwork model is established, and the established laryngeal disease diagnosis network model can be used for an intelligent system for laryngeal disease diagnosis, so that laryngoscope images are better diagnosed, doctors are helped to improve the diagnosis efficiency and diagnosis accuracy of diseases, and the missed diagnosis rate and the misdiagnosis rate are reduced. The laryngeal disease diagnosis system can be applied to intelligent detection of laryngoscope images.

Description

technical field [0001] The invention relates to the interdisciplinary field of combining artificial intelligence and medical diagnosis, in particular to a laryngeal disease diagnosis system based on a deep learning neural network. Background technique [0002] Due to the special position and complex physiological structure of the human throat, it is often impossible to directly spy on it. When diagnosing diseases of the larynx, doctors often obtain internal information by inserting a laryngoscope into the larynx to take images, and then carry out diagnosis and treatment. In clinical practice, fiberoptic laryngoscopy is often used for diagnosis and treatment. Fiber laryngoscope is a kind of fiber optic device, which causes less trauma and helps to reduce the pain of patients. It can also enlarge the lesion site through fiber imaging technology, provide a clear field of view, and help doctors make better judgments. [0003] Laryngoscopy can usually be used for initial screen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G16H50/20
CPCG06N3/08G16H50/20G06N3/045G06F18/241G06F18/214
Inventor 赵雪岩罗浩刘绍宠刘富豪蒋宇辰尹珅
Owner HARBIN INST OF TECH
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