Automatic Detection of Lumbar Disc Herniation Using YOLOv7

Ardha Ardea Prisilla, Yori Pusparani, Wen Thong Chang, Ben Yi Liau, Yih Kuen Jan, Peter Ardhianto, Chih Yang Lin, Chi Wen Lung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages443-444
Number of pages2
ISBN (Electronic)9798350324174
DOIs
StatePublished - 2023
Event2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan
Duration: 17 Jul 202319 Jul 2023

Publication series

Name2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Country/TerritoryTaiwan
CityPingtung
Period17/07/2319/07/23

Keywords

  • Automatic Detection
  • Deep Learning
  • Low Back Pain
  • MRI

Fingerprint

Dive into the research topics of 'Automatic Detection of Lumbar Disc Herniation Using YOLOv7'. Together they form a unique fingerprint.

Cite this