@inproceedings{484117819e35468fb369f8c43355541f,
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 Chang, {Wen Thong} and Liau, {Ben Yi} and Jan, {Yih Kuen} and Peter Ardhianto and Lin, {Chih Yang} and Lung, {Chi Wen}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 ; Conference date: 17-07-2023 Through 19-07-2023",
year = "2023",
doi = "10.1109/ICCE-Taiwan58799.2023.10226718",
language = "???core.languages.en_GB???",
series = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "443--444",
booktitle = "2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings",
}