Measurement Model for Medical Image Feature Matrix Similarity Based on CNN

Abstract

The original similarity measurement model is easy to ignore the processing of image details, resulting in poor accuracy of similarity measurement. In the paper, we propose a similarity measurement model for the medical image feature matrix based on the convolutional neural network (CNN). First, the Gaussian convolution kernel is used to obtain the global and local feature data of medical images, and the corresponding data set is formed. Second, the convolution layer of CNN is introduced, and the image feature matrix is obtained by the convolution layer. Finally, the similarity measurement model of the medical image feature matrix is constructed. The results show that the image similarity measurement effect of this model is better when the test process is divided into three parts: global, local, and detail. The highest error rate of the proposed algorithm is only about 0.21, which takes less time, and the overall fitting degree can reach about 91%. Compared with traditional methods, the accuracy of image similarity measurement is higher and the use effect is better.