CT and MRI fusion diagnosis technology based on recurrent neural network

Abstract

With the continuous progress of digital medicine and smart medical technology, the application of computer vision technology in medical image processing is also constantly developing and advancing. Image fusion technology in computer vision technology can complement the advantages of medical imaging through effective algorithms, discover useful and valuable information in medical diagnosis, and it is very effective in compensating for the shortcomings of medical image presentation technology and the lack of image information. The fusion of CT medical images and MRI medical images using computer vision technology can combine the advantages of CT images displaying clear bone information and high image resolution with the advantages of MRI images displaying clear soft structures, forming complementary advantages, thus making the display information of medical images more abundant. This article mainly utilizes wavelet transform algorithm to fuse CT and MRI anatomical imaging and proposes a medical image classification and diagnosis method based on local spatial sequence recurrent neural network (RNN), extracting higherlevel semantic features of images with better representativeness. This method extracts the local spatial sequence features of the samples and utilizes a temporal RNN to highly integrate and abstract the local spatial information. While obtaining high-level semantic features, it enhances the role of useful pixels in the local space and suppresses the influence of useless pixels. The experimental results show that the proposed method improves classification accuracy.