Breast tumor classification device and storage medium based on discriminative convolutional neural network

Abstract

The invention discloses a breast tumor classification device and a storage medium based on a discriminative convolutional neural network. The method includes: segmenting tumors in multiple ultrasound images to obtain a region of interest and performing data augmentation to obtain a training set Construct a discriminative convolutional neural network model, calculate the model parameters of the discriminative convolutional neural network based on the training image; wherein, the structure of the discriminative convolutional neural network model is: on the basis of the convolutional neural network Add a differentiating auxiliary branch, access the convolutional layer, pooling layer and fully connected layer, and introduce the Inter-intra Loss function to enhance the similarity between the same class and the difference between different classes; get a picture to be Classified breast ultrasound images, segmenting the ultrasound images to obtain a region of interest; inputting the region of interest into the discriminative convolutional neural network to obtain a classification result. The classification method of the invention can effectively improve the performance of tumor classification in breast ultrasound images.

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 device and storage medium 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-aide...

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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