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

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.