TY - GEN
T1 - Automatic Detection of Lumbar Disc Herniation Using YOLOv7
AU - Prisilla, Ardha Ardea
AU - Pusparani, Yori
AU - Chang, Wen-Thong
AU - Liau, Ben-Yi
AU - Jan, Yih-Kuen
AU - Ardhianto, Peter
AU - Lin, Chih-Yang
AU - Lung, Chi-Wen
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023
Y1 - 2023
N2 - The detection of lumbar disc herniation (LDH) through magnetic resonance imaging (MRI) poses a challenge due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. One potential solution is using deep learning methods to identify lumbar abnormalities in MRI images automatically. The YOU ONLY LOOK ONCE (YOLO) model series has gained popularity for training deep learning algorithms for real-time biomedical image detection. This study aims to assess the performance of the latest YOLOv7 in detecting LDH across different regions of the lumbar intervertebral disc. The analysis revealed that YOLOv7 exhibits a poor performance and low detection rate of LDH across the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 regions.
AB - The detection of lumbar disc herniation (LDH) through magnetic resonance imaging (MRI) poses a challenge due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. One potential solution is using deep learning methods to identify lumbar abnormalities in MRI images automatically. The YOU ONLY LOOK ONCE (YOLO) model series has gained popularity for training deep learning algorithms for real-time biomedical image detection. This study aims to assess the performance of the latest YOLOv7 in detecting LDH across different regions of the lumbar intervertebral disc. The analysis revealed that YOLOv7 exhibits a poor performance and low detection rate of LDH across the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 regions.
KW - Automatic Detection
KW - Deep Learning
KW - Low Back Pain
KW - MRI
UR - http://www.scopus.com/inward/record.url?scp=85174968612&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174968612&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan58799.2023.10226718
DO - 10.1109/ICCE-Taiwan58799.2023.10226718
M3 - Conference contribution
SP - 843
EP - 844
BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
ER -