Breast tumor classification algorithm based on convolutional neural network VGG16

Abstract

The invention relates to a breast tumor classification algorithm based on a convolutional neural network VGG16. The algorithm comprises the following steps that: data preprocessing: for a dataset which presents a data imbalance state, carrying out imbalance processing and data enhancement processing; the establishment of the convolutional neural network: 1) network pre-training: utilizing the VGG16 to carry out network training on an ImageNet large natural image dataset, and storing trained weight; 2) network key node selection: utilizing different layers of the VGG16 network to carry out feature extraction on a breast tumor DDSM (Digital Database for Screening Mammography) dataset, applying the same SVM (Support Vector Machine) classifier for classification for extracted features, and selecting a layer with highest classification performance as a node constructed by a new network; and 3) connecting two layers of full connection and one layer of softmax to form a new network behind thenode constructed by the selected network; and carrying out migration learning.

Description

technical field [0001] The invention belongs to the field of image classification, and relates to an image classification algorithm based on a basic VGG16 convolutional neural network, which can be used for the classification task of medical images. Background technique [0002] In recent years, due to its high curative effect, breast cancer has gradually become a major cause of death among women. The main cause of its disease is the deterioration of malignant tumor cells in breast tissue. However, in medical research, it is still a medical blind spot to propose an effective cure for the pathogenesis of breast tumors. Therefore, early diagnosis of tumors has become the most effective way to prevent breast cancer. [0003] However, the current common research method is to classify tumors based on traditional machine learning methods. However, the classification performance improvement designed by the current machine learning method encounters a bottleneck, and the room for ...

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Problems solved by technology

However, the classification performance improvement designed by the current machine learning method encounters a bottleneck, and the space for performance improvement is limited.

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