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 contribution


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 (US)
Title of host publication2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
Number of pages2
ISBN (Electronic)9798350324174
StatePublished - 2023


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

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Information Systems
  • Instrumentation
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Media Technology


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