Method and device for automatically diagnosing benign and malignant thyroid nodules

A technology for automatic diagnosis of thyroid nodules, which is applied in medical automated diagnosis, neural learning methods, image data processing, etc., can solve problems such as poor automatic classification of thyroid nodules in benign and malignant examinations

Pending Publication Date: 2021-05-18
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

[0004] The purpose of the present invention is to provide a method and device for automatically diagnosing benign and malignant thyroid n...

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  • Method and device for automatically diagnosing benign and malignant thyroid nodules
  • Method and device for automatically diagnosing benign and malignant thyroid nodules
  • Method and device for automatically diagnosing benign and malignant thyroid nodules

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Embodiment 1

[0050] According to an embodiment of the present invention, a method for automatically diagnosing benign and malignant thyroid nodules is provided, figure 1 It is a flowchart of a method for automatically diagnosing benign and malignant thyroid nodules in an embodiment of the present invention, such as figure 1 As shown, the method for automatically diagnosing benign and malignant thyroid nodules according to an embodiment of the present invention specifically includes:

[0051] Step 1. Perform noise reduction preprocessing on the original thyroid ultrasound image; the noise reduction preprocessing specifically includes: grayscale processing the image to make the threshold value 0 to obtain a binarized image; on the basis of the binarized image, The image opening operation is performed to corrode the noise area of ​​the image to complete the noise reduction preprocessing.

[0052] Step 2, data enhancement is performed on the preprocessed thyroid ultrasound image; data enhance...

Embodiment 2

[0084] An embodiment of the present invention provides a device for automatically diagnosing benign and malignant thyroid nodules, such as image 3 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:

[0085] Step 1, performing noise reduction preprocessing on the original thyroid ultrasound image;

[0086] Step 2, performing data enhancement on the preprocessed thyroid ultrasound image;

[0087] Step 3, putting the data-enhanced thyroid ultrasound images into three convolutional neural network models pre-built by ResNet or DenseNet or ResNext for training;

[0088] Step 4, model integration is performed on the images trained by different models in step 3.

[0089] Further, the denoising preprocessing of the original thyroid ultrasound image specifically includes:

[0090] The image is graysca...

Embodiment 3

[0103] 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 method steps described in the first method embodiment are implemented, I won't repeat them here.

[0104] The computer-readable storage medium described in this embodiment includes but is not limited to: ROM, RAM, magnetic disk or optical disk, and the like.

[0105] Obviously, those skilled in the art should understand that each module or each step of the above-mentioned present invention can be realized by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed in a network formed by multiple computing devices Alternatively, they may be implemented in program code executable by a computing device so that they may be stored in a storage device to be executed by a computing...

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Abstract

The invention discloses a method and a device for automatically diagnosing benign and malignant thyroid nodules. The method comprises the following steps: step 1, carrying out noise reduction preprocessing on an original thyroid ultrasound image; 2, performing data enhancement on the preprocessed thyroid ultrasound image; 3, the thyroid ultrasound image after data enhancement being respectively put into three convolutional neural network models which are constructed through ResNet, DenseNet or ResNext in advance for training; and 4, carrying out model integration on images trained by different models in the step 3. According to the method, the problem that the automatic classification effect of benign and malignant thyroid nodules is poor is solved, the deep learning image automatic recognition technology is applied, the latest framework is used for extracting image features, the automatic classification task of benign and malignant thyroid nodules is completed in an end-to-end mode, and meanwhile high accuracy is guaranteed.

Description

technical field [0001] The invention relates to the field of deep learning of computer artificial intelligence, in particular to a method and device for automatically diagnosing benign and malignant thyroid nodules. Background technique [0002] Thyroid nodules are very common in the general population. With the help of thyroid ultrasonography, data show that approximately 20% to 76% of adults have nodules in the thyroid. Although most thyroid nodules are benign nodules, the presence and number of nodules increase the probability of lesions. Therefore, in order to reduce the morbidity, it is particularly important to check the thyroid gland in advance and screen for benign and malignant thyroid nodules. With the development of the times, the classification techniques of thyroid nodules are constantly evolving and improving. In the early stage, due to the complex and diverse ultrasound images of thyroid nodules, the diagnostic modes of benign and malignant thyroid nodules ...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H30/20G06T7/00G06T5/00G06N3/04G06N3/08G06K9/62
CPCG16H50/20G16H30/20G06T7/0012G06T5/002G06N3/08G06T2207/10132G06T2207/30096G06N3/045G06F18/24
Inventor 杜强严亚飞郭雨晨聂方兴唐超张兴
Owner 北京小白世纪网络科技有限公司
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