CT and MRI fusion diagnosis technology based on recurrent neural network

Bo Liu; Zhanyao Ma

doi:10.1117/12.3026810

15 March 2024 CT and MRI fusion diagnosis technology based on recurrent neural network

Bo Liu, Zhanyao Ma

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751W (2024) https://doi.org/10.1117/12.3026810
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

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 higher-level 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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bo Liu and Zhanyao Ma "CT and MRI fusion diagnosis technology based on recurrent neural network", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751W (15 March 2024); https://doi.org/10.1117/12.3026810

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KEYWORDS

Medical imaging

Magnetic resonance imaging

Education and training

Image fusion

Neural networks

Computer vision technology

Deep learning

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