LUMBAR SPINAL STENOSIS ANALYSIS WITH DEEP LEARNING BASED DECISION SUPPORT SYSTEMS

Lumbar spinal stenosis is a disease with negative consequences and usually occurs in 3 vertebrae, disc and canal located in the lower back. In this region, the nerves in the canal can be exposed to pressure for various reasons, and diseases occur. Surgical operation is required to treat canal narrowing, and the exact location and size of the spinal stenosis is vital importance for the operation. The UNet model, which is an example of this network, can be further deeper using different deep learning networks. In this study, it is aimed to be the basis for the creation of a system that helps in the diagnosis of canal stenosis by using a deeper network. The ResUNET model, in which ResNet is used as the backbone, achieved an average IoU of 0.987. This result reveals that MR images can be used in segmentation for the diagnosis of Lumbar spinal stenosis.