Breast structure disorder identification method based on two transfer and convolutional neural network

A neural network recognition and neural network technology, applied in the field of structural disorder recognition based on deep learning, can solve problems such as difficulties, achieve good classification effect and performance, and avoid limitations and subjectivity.

Inactive Publication Date: 2018-02-02
WUHAN UNIV OF SCI & TECH
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Problems solved by technology

[0006] Since the training of convolutional neural network requires a large number of samples, it

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  • Breast structure disorder identification method based on two transfer and convolutional neural network
  • Breast structure disorder identification method based on two transfer and convolutional neural network
  • Breast structure disorder identification method based on two transfer and convolutional neural network

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Embodiment Construction

[0027] The present invention is a kind of method based on twice migration convolutional neural network to identify the pathological disorder of mammary gland structure, combined below figure 1 Describe its specific implementation process.

[0028] Step 1. Data Augmentation

[0029] The data enhancement technology mainly used in the present invention is a geometric transformation method, which mainly includes translation (translate the image in a certain way on the image plane), rotation (rotate the image at a certain angle at random, and change the orientation of the image content) and scaling (according to a certain scale up or down the image). The main purpose of using data augmentation is to overcome the problem of insufficient number of samples in breast malignant masses and structural disorders, so as to avoid overfitting.

[0030] Step 2, the first transfer learning

[0031] To solve the problem of insufficient number of structurally disordered samples, we introduce a...

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Abstract

The invention discloses a breast structure disorder identification method based on two transfer and a convolutional neural network. The method is characterized by transferring model parameters trainedin an ImageNet data set to a target convolutional neural network to carry out initialization on model parameters thereof ( first transfer); then, carrying out first fine-tuning training (second transfer) on a target convolutional neural network model through malignant tumor images and normal breast tissue images; carrying out second fine-tuning training through class-information-known structure disorder images and normal breast tissue images in a training set; and finally, carrying out identification on class-information-unknown structure disorder and normal tissues. The method realizes identification based on the convolutional neural network and transfer learning; and compared with the existing method, the method achieves better classification effect and performance.

Description

technical field [0001] The present invention relates to automatic recognition of structural disorder lesions in mammography, in particular to a structural disorder recognition method based on deep learning (convolutional neural network and migration learning). Background technique [0002] Breast cancer is a common tumor that threatens women's physical and mental health. Early detection and treatment are the key to reducing the risk of breast cancer, and mammography is the most widely used screening method. Calcification, mass and structural disorder are the three most common X-ray abnormalities in breast cancer, and structural disorder is the most difficult lesion to detect. Among the currently used medical imaging technologies, low-dose X-ray mammography is an effective tool for early breast cancer detection, and is the most commonly used clinical detection method for female breast cancer. [0003] Structural disorder is an early and very important sign of breast cancer....

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0012G06T2207/10116G06T2207/20084G06T2207/20081G06V2201/03G06F18/24G06F18/214
Inventor 刘小明翟蕾蕾付天宇胡威刘俊张凯
Owner WUHAN UNIV OF SCI & TECH
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