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

doi:10.1109/ICCE-Taiwan58799.2023.10226718

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 proceeding › Conference contribution

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 language	English (US)
Title of host publication	2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
Pages	843-844
Number of pages	2
ISBN (Electronic)	9798350324174
DOIs	https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226718
State	Published - 2023

Keywords

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

Online availability

10.1109/ICCE-Taiwan58799.2023.10226718

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title = "Automatic Detection of Lumbar Disc Herniation Using YOLOv7",

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.",

keywords = "Automatic Detection, Deep Learning, Low Back Pain, MRI",

author = "Prisilla, {Ardha Ardea} and Yori Pusparani and Wen-Thong Chang and Ben-Yi Liau and Yih-Kuen Jan and Peter Ardhianto and Chih-Yang Lin and Chi-Wen Lung",

note = "ACKNOWLEDGMENT The Lumbar Spine MRI Dataset is available through Mendeley Data at http://dx.doi.org/10.17632/k3b363f3vz.2, reference number [5]. This study was supported by the Ministry of Science and Technology of the Republic of China (MOST111-2221-E-468-002).",

year = "2023",

doi = "10.1109/ICCE-Taiwan58799.2023.10226718",

language = "English (US)",

pages = "843--844",

booktitle = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",

}

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 - ACKNOWLEDGMENT The Lumbar Spine MRI Dataset is available through Mendeley Data at http://dx.doi.org/10.17632/k3b363f3vz.2, reference number [5]. This study was supported by the Ministry of Science and Technology of the Republic of China (MOST111-2221-E-468-002).

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

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M3 - Conference contribution

SP - 843

EP - 844

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

ER -

Automatic Detection of Lumbar Disc Herniation Using YOLOv7

Abstract

Keywords

ASJC Scopus subject areas

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