Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN

• Published in: Diagnostics (Basel) Vol. 13; no. 18; p. 2975
• Main Authors: Shahzadi, Turrnum, Ali, Muhammad Usman, Majeed, Fiaz, Sana, Muhammad Usman, Diaz, Raquel Martínez, Samad, Md Abdus, Ashraf, Imran
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2023; MDPI
• Subjects: Artificial intelligence; Automation; Back pain; Backache; Datasets; Decision-making; Deep learning; Image processing; lumbar spine stenosis; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Neural networks; Spinal cord; Stenosis; Success; Vertebrae; X-rays; United Kingdom; United Kingdom > UK
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13182975

Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient’s lumbar spine. Currently, the versatility and accuracy of LSS segmentation algorithms are limited. The objective of this research is to use magnetic resonance imaging (MRI) to automatically categorize LSS. This study presents a convolutional neural network (CNN)-based method to detect LSS using MRI images. Radiological grading is performed on a publicly available dataset. Four regions of interest (ROIs) are determined to diagnose LSS with normal, mild, moderate, and severe gradings. The experiments are performed on 1545 axial-view MRI images. Furthermore, two datasets—multi-ROI and single-ROI—are created. For training and testing, an 80:20 ratio of randomly selected labeled datasets is used, with fivefold cross-validation. The results of the proposed model reveal a 97.01% accuracy for multi-ROI and 97.71% accuracy for single-ROI. The proposed computer-aided diagnosis approach can significantly improve diagnostic accuracy in everyday clinical workflows to assist medical experts in decision making. The proposed CNN-based MRI image segmentation approach shows its efficacy on a variety of datasets. Results are compared to existing state-of-the-art studies, indicating the superior performance of the proposed approach.