Breast tumor classification method based on differentiated convolutional neural network and breast tumor classification device based on differentiated convolutional neural network

Abstract

The invention discloses a breast tumor classification method based on a differentiated convolutional neural network and a breast tumor classification device based on a differentiated convolutional neural network. The method comprises the steps that the tumor in multiple ultrasonic images is segmented to acquire an area of interest, and data augmentation is performed so that a training set is obtained; a differentiated convolutional neural network model is constructed, and the model parameters of the differentiated convolutional neural network are calculated based on training images, wherein the structure of the differentiated convolutional neural network model is that differentiated auxiliary branches are additionally arranged on the basis of the convolutional neural network, a convolutional layer, a pooling layer and a full connection layer are accessed, and an Inter-intra Loss function is introduced for increasing the similarity between the same classes and the differentiation between different classes; a breast ultrasonic image to be classified is acquired, the ultrasonic image is segmented and the area of interest is acquired; and the area of interest is inputted to the differentiated convolutional neural network so as to obtain the classification result. According to the classification method, the tumor classification performance in the breast ultrasonic image can be effectively enhanced.

Description

technical field [0001] The invention belongs to the field of data classification for medical images, and in particular relates to a breast tumor classification method and device based on a discriminative convolutional neural network. Background technique [0002] For women, breast cancer is one of the diseases with higher morbidity and mortality. Early detection and early treatment is the key to improving treatment efficiency. Medical imaging has become the main way to assist in clinical diagnosis of diseases. Compared with other images such as mammography and nuclear magnetic resonance, ultrasound has the advantages of less radiation, low price, and sensitivity to dense tissue detection. Therefore, ultrasound images have become one of the main tools to assist in the early diagnosis of breast cancer. [0003] Due to the different experience of radiologists, manual diagnosis of breast ultrasound images has a certain degree of subjectivity. The use of computer-aided diagno...

AI Technical Summary

Problems solved by technology

[0006] However, existing feature extraction and classification methods have certain limitations

In terms of feature extraction, although texture features are an important clinical distinguishing feature of benign and malignant tumors, there are still some unknown image features that can be used to classify tumors. Satisfactory classification performance

In addition, the generalization performance of artificially designed features is poor, and images (different data) for different devices need to be redesigned

In terms of classification, most of the existing classifiers are shallow models, and it is difficult to fully learn the effective information of the features.

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