eprintid: 8999 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/89/99 datestamp: 2023-09-28 23:30:23 lastmod: 2023-09-28 23:30:24 status_changed: 2023-09-28 23:30:23 type: article metadata_visibility: show creators_name: Shahzadi, Turrnum creators_name: Ali, Muhammad Usman creators_name: Majeed, Fiaz creators_name: Sana, Muhammad Usman creators_name: Martínez Díaz, Raquel creators_name: Samad, Md Abdus creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: raquel.martinez@uneatlantico.es creators_id: creators_id: title: Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN ispublished: pub subjects: uneat_bm subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: lumbar spine stenosis; magnetic resonance imaging; deep learning; image processing 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. date: 2023-09 publication: Diagnostics volume: 13 number: 18 pagerange: 2975 id_number: doi:10.3390/diagnostics13182975 refereed: TRUE issn: 2075-4418 official_url: http://doi.org/10.3390/diagnostics13182975 access: open language: en citation: Artículo Materias > Biomedicina Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Shahzadi, Turrnum; Ali, Muhammad Usman; Majeed, Fiaz; Sana, Muhammad Usman; Martínez Díaz, Raquel; Samad, Md Abdus y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, raquel.martinez@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN. Diagnostics, 13 (18). p. 2975. ISSN 2075-4418 document_url: http://repositorio.unini.edu.mx/id/eprint/8999/1/diagnostics-13-02975.pdf