Shahzadi, Turrnum and Ali, Muhammad Usman and Majeed, Fiaz and Sana, Muhammad Usman and Martínez Díaz, Raquel and Samad, Md Abdus and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, raquel.martinez@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2023) Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN. Diagnostics, 13 (18). p. 2975. ISSN 2075-4418
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Abstract
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.
| Item Type: | Article |
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| Uncontrolled Keywords: | lumbar spine stenosis; magnetic resonance imaging; deep learning; image processing |
| Subjects: | Subjects > Biomedicine Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 28 Sep 2023 10:38 |
| Last Modified: | 28 Sep 2023 10:38 |
| URI: | http://repositorio.funiber.org/id/eprint/8999 |
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