Convolution neural network training method, ultrasonic image recognition and location method and system

Abstract

The present invention relates to a convolution neural network training method and an ultrasonic image recognition and location method and system. The method comprise the following steps of establishing a convolution neural network model; taking the convolution neural network model trained on ImageNet as the starting point, reading the B-ultrasonic images in the training data set by different network nodes / layers, and then fine turning the B-ultrasonic images to carry out the migration learning of the neural network model, wherein the images in the training data set come from B-ultrasound examination images of a single center and multiple machines, and are marked with pathological examination results; determining the object position and classification by regression. The invention provides an artificial intelligent identification and positioning system for realizing ultrasonic images, such as ultrasonic images of thyroid nodules, by using migration learning and depth convolution neural network, which greatly improves the accuracy of a model and realizes the shaping of a benign and malignant thyroid nodules auxiliary evaluation and positioning system thereof.

Description

technical field [0001] The invention relates to the technical field of image analysis, in particular to a convolutional neural network training method, an ultrasonic image recognition and positioning method and system. Background technique [0002] Statistics from 2003 to 2011 in my country show that the incidence and death of thyroid cancer increased at an annual rate of 20.1% and 1.6%, respectively, and the incidence of thyroid cancer in all tumor registration points in the country showed an upward trend. Worldwide, the incidence of thyroid cancer has continued to rise over the past few decades, especially among women, showing explosive growth. Thyroid nodules have become a common clinical problem. Ultrasound is the most commonly used method for differential diagnosis of thyroid cancer. It is non-invasive, simple, and economical, and has been widely used. The only disadvantage is that it requires relatively high requirements for examiners and requires experienced Only sen...

AI Technical Summary

Problems solved by technology

Thyroid nodules have become a common clinical problem. Ultrasound is the most commonly used method for differential diagnosis of thyroid cancer. It is non-invasive, simple, and economical, and has been widely used. The only disadvantage is that it requires relatively high requirements for examiners and requires experienced Only senior physicians can make an accurate assessment. Ultrasonography can assess the risk of benign and malignant thyroid glands, which determines whether people with thyroid nodules need further fine-needle aspiration biopsy or surgical treatment, and the training of experienced senior physicians takes a period of time. Long, uneven distribution of medical resources in various regions

In addition, the clinical work of the diagnostician is heavy, the repetitive inspection work is low, the inspection efficiency is low, the fatigue is easy, the manual error is easy to be made, and the cognition of different doctors leads to differences in the inspection results.

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