Abstract
In this paper, we propose a new method for CT pathological image analysis of brain and chest to extract image features and classify images. Because the deep neural network needs a large number of labeled samples to complete the training, and the cost of medical image labeling is very high, the training samples needed to train the deep neural network are insufficient. In this paper, a semi supervised learning based image classification method is proposed, which uses a small amount of labeled pathological image data to train the network model, and then integrates the features extracted by the network to classify the image. The results show that the classification effect of the neural network is better than convolution neural network and other traditional image classification models. To some extent, it can reduce the dependence of neural network on a large number of training samples, and effectively reduce the over fitting phenomenon of the network. Through the analysis of the overall classification accuracy and kappa coefficient of different classification methods under different sample numbers, it is found that the overall classification accuracy and kappa coefficient are increasing with the increasing number of training samples. Especially in the case of a small number of training samples, compared with other deep neural networks and traditional classification methods, the classification accuracy of the counter neural network is about 10% higher than that of other neural networks and traditional classification methods, and the advantages are more obvious.
Highlights
With the increasing demand for faster and more accurate treatment, medical imaging plays an increasingly important role in the early detection, diagnosis and treatment of diseases
TRADITIONAL CLASSIFICATION METHOD In order to compare the accuracy of traditional classification method and deep learning classification method for image classification, the traditional classification method adopts support vector machine algorithm, and deep learning algorithm selects neural network for confrontation, and compares the image classification effect of Support vector machine (SVM) and generative adversarial network (GAN)
In order to ensure the accuracy of classification, here we select scale invariant feature transform (SIFT) feature as the description operator, and support vector machine as the statistical classifier
Summary
With the increasing demand for faster and more accurate treatment, medical imaging plays an increasingly important role in the early detection, diagnosis and treatment of diseases. In the experiment, it can be divided many times, trained on the corresponding data, and average the results of all test sets as the generalization error estimation of the final draft of the model.
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