Lumbar disc herniation typing method based on double-branch fusion and multiscale attention

Zongming Zhang; Hong Shao

doi:10.1117/12.3034774

11 July 2024 Lumbar disc herniation typing method based on double-branch fusion and multiscale attention

Zongming Zhang, Hong Shao

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321039 (2024) https://doi.org/10.1117/12.3034774
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Lumbar intervertebral disc herniation is a common degenerative disease of the spine, and the application of deep learning technology to lumbar intervertebral disc herniation typing helps to improve the diagnostic efficiency of doctors and reduce the burden of doctors on tedious work. In this paper, a lumbar disc herniation typing method based on the ResNet50 network model is proposed. Firstly, to address the problem of a large-scale gap between different herniation types of lumbar disc foci, a multi-scale attention mechanism is introduced into the backbone network to improve the model's ability to capturing feature information of different scales; based on this, a double-branch structural model is constructed, with sagittal and axial positions as inputs to improve the robustness of the model by integrating information from different perspectives. information from different perspectives to improve the robustness and generalization ability of the model. The experimental results show that the method has a high accuracy of 88.1% in the test set.

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

Citation Download Citation

Zongming Zhang and Hong Shao "Lumbar disc herniation typing method based on double-branch fusion and multiscale attention", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321039 (11 July 2024); https://doi.org/10.1117/12.3034774

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