Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 86-95

Original Article

Detection of Lumbar Spine Stenosis in MRI Spinal Imaging

Mohammed A. Abed 1 Z. T.Al-Qaysi 1 Aws Q.Hamdi 2 Salwa K.Abdulateef 1 M. A.Ahmed 1 Mahmood M. Salih 1
1 Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
2 Department of Radiology, College of Medicine, Tikrit University, Tikrit, Iraq
History
  • Received: December 28, 2023
  • Revised: March 2, 2024
  • Accepted: April 3, 2024
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.9

Abstract

Lumbar spine stenosis (LSS) is a common reason for low back pain, which refers to anatomical spinal canal stenosis. It often causes pressure on the nerve elements due to the surrounding soft tissue and bone. Due to the huge number of spinal injuries, manual diagnosis of lumbar spine stenosis by radiologists is tedious or time-consuming. Therefore, Deep learning techniques have become a more helpful tool to overcome this problem. For this purpose, this study employed the YOLO-v5 to develop an LSS detection model on a dataset of lumbar spine MRI scans from 153 patients with symptomatic low back pain. The dataset was filtered to include 84 mid-sagittal images using annotation techniques. The detection model is utilized to classify the intervertebral disc (IVD) condition as either bulging or normal. The results obtained showed that the model achieved an accuracy exceeding 88% in detecting and classifying the lumbar spine vertebra. The developed models showed that they are effective for lumbar intervertebral disc classification.

Keywords

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