基于卷积神经网络与特征选择的医疗图像误差预测算法

In order to address the problem that traditional medical image error prediction algorithm can not select image features well, there are some problems such as low fitting degree of image error prediction value, low actual value and long prediction time, a medical image error prediction algorithm based on convolution neural network and feature selection was proposed. Firstly, five integrated rules were selected to construct adaptive multi-classifiers to classify medical image regions. Secondly, the training convolution neural network was used to extract different types of medical image area features by using the training neural network. Then, multiple evaluation criteria were combined to generate special features. The optimal feature subset was searched to complete the feature selection of suspicious region image. Finally, the multiple linear regression matrix between the prediction sample and the training sample was established to realize the error prediction by taking the pixel points of the feature region as the training sample. The experimental results show that the proposed algorithm has high fitness of integration rules and good classification performance, the accuracy of region distance calculation is about 95%, the AUC value of feature selection is high, and the fitting degree and prediction time of the prediction results are better than those of the traditional algorithm.