Breast ultrasonic tumor recognition method based on deep learning

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...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above-mentioned deficiencies in the prior art, a breast ultrasound tumor recognition method based on deep learning provided by the present invention solves the problem of slow recognition speed of the existing breast ultrasound tumors

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