Breast ultrasonic tumor recognition method based on deep learning

A technology of deep learning and identification method, applied in the field of breast ultrasound tumor identification based on deep learning, can solve the problems of slow breast ultrasound tumor identification, and achieve the effects of short identification time, prevention of overfitting, and simple interface

Active Publication Date: 2019-09-20
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0005] Aiming at the above-mentioned deficiencies in the prior art, a breast ultrasound tumor recognition method based on deep

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  • Breast ultrasonic tumor recognition method based on deep learning

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[0037] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0038] Such as figure 1 As shown, the breast ultrasound tumor recognition method based on deep learning includes the following steps:

[0039] S1. Carry out benign and malignant labeling on breast ultrasound images of existing cases, and obtain labeled breast ultrasound images;

[0040] S2. Preprocessing the labeled breast ultrasound image to obtain a preprocessed image;

[0041] S3, adopting the convolutional neural netw...

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Abstract

The invention discloses a breast ultrasound tumor recognition method based on deep learning. The breast ultrasound tumor recognition method comprises the following steps: S1, carrying out benign and malignant labeling on a breast ultrasound image of an existing case; S2, preprocessing the labeled breast ultrasound image; S3, acquiring the characteristics of the preprocessed image by adopting a convolutional neural network model; S4, taking the obtained features and the corresponding labels as training data to train different classification models respectively; S5, fusing all the trained classification models by adopting a stacking method; and S6, taking the breast ultrasound tumor to be identified as the input of the fused model, and completing the identification according to the output result. According to the method, the image recognition result can be directly obtained only by putting the breast ultrasound image to be recognized, the recognition time is short, diagnosis can be carried out through the connection server, or the breast ultrasound image can be directly deployed in a local computer, flexibility is high, an interface is simple, operation is easy, and user friendliness is achieved.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a breast ultrasound tumor recognition method based on deep learning. Background technique [0002] When a breast ultrasound image is given, the doctor will first diagnose the image to determine whether there is a tumor in the image and whether it is a benign or malignant tumor, so as to give a general direction for subsequent treatment. The existing implementation of the same function usually gives identification suggestions based on the comprehensive medical assistance system in commercial software. [0003] The general working principle of the medical comprehensive auxiliary system is as follows: the provider of the commercial comprehensive medical auxiliary system uses a variety of decision-based processes to determine whether an image is a benign or malignant tumor based on its own local data. The software provider comes with manual feature extraction, and then performs algo...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0012G06T2207/10132G06T2207/20081G06T2207/30068G06T2207/30096G06F18/2411
Inventor 杨国武陈琴陈祥
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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