Medical image classification method for combining deep characteristic extraction and shallow characteristic extraction

Abstract

The invention relates to a medical image classification method for combining deep characteristic extraction and shallow characteristic extraction. The medical image classification method comprises the following steps: training a deep convolutional neural network model, a bag-of-word model and a BP neural network at first, then dividing a medical image to be classified into image sub blocks, inputting the image sub blocks into the deep convolutional neural network model, the bag-of-word model and the BP neural network, which are trained, in sequence to obtain the type of each image sub block, and classifying the medical image to be classified based on the majority voting principle.

Description

technical field [0001] The invention relates to an image classification method, especially for medical image classification problems with large intra-class differences (such as CT images from different parts of the body) and small inter-class differences (such as CT images and MRI images). The feature extraction of the convolutional neural network and the visual feature extraction guided by domain knowledge, and the combination of the obtained deep and shallow features to train the classifier, better achieve medical image classification. Background technique [0002] Image classification mainly includes two main links: feature extraction and classifier construction. Among them, feature extraction aims to transform the image into a set of numerical features that are convenient for subsequent processing, which is the basis of classifier construction and is particularly important in solving image classification problems. Traditional visual features generally include image colo...

AI Technical Summary

Problems solved by technology

[0005] In order to avoid the poor effect of the classification algorithm based on deep learning caused by the small number of medical images in the prior art, and the low correct rate of the traditional classification method based on visual features, the present invention proposes a combination of deep feature extraction and shallow Medical Image Classification Method Based on Feature Extraction

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