Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

Abstract

Background: Lumbar disc herniation (LDH) is among the most common causes of lower back pain and sciatica. The causes of LDH have not been fully elucidated but most likely involve a complex combination of mechanical and biological processes. Magnetic resonance imaging (MRI) is a tool most frequently used for LDH because it can show abnormal soft tissue areas around the spine. Deep learning models may be trained to recognize images with high speed and accuracy to diagnose LDH. Although the deep learning model requires huge numbers of image datasets to train and establish the best model, this study processed enhanced medical image features for training the small-scale deep learning dataset.Methods: We propose automatic detection to assist the initial LDH exam for lower back pain. The subjects were between 20 and 65 years old with at least 6 months of work experience. The deep learning method employed the YOLOv3 model to train and detect small object changes such as LDH on MRI. The dataset images were processed and combined with labeling and annotation from the radiologist’s diagnosis record.Results: Our method proves the possibility of using deep learning with a small-scale dataset with limited medical images. The highest mean average precision (mAP) was 92.4% at 550 images with data augmentation (550-aug), and the YOLOv3 LDH training was 100% with the best average precision at 550-aug among all datasets. This study used data augmentation to prevent under- or overfitting in an object detection model that was trained with the small-scale dataset.Conclusions: The data augmentation technique plays a crucial role in YOLOv3 training and detection results. This method displays a high possibility for rapid initial tests and auto-detection for a limited clinical dataset.

A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images

“…In addition, using more data validation sets over 3500 images may increase YOLO performance [ 93 ]. However, the current study using a small-scale validation set under 350 images showed good performance [ 42 ]. Therefore, this study used a small-scale validation set using plantar pressure images and achieved a suitable YOLO performance.…”

“…Using deep learning for object detection is widely used in biomedical applications [ 40 , 41 , 42 ]. For example, the deep learning model can identify plantar pressure patterns for early abnormal detection of foot problems [ 43 ].…”

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“…Based on profiles of paravertebral muscles, the Image J 1.53 software program (United States National Institutes of Health, Bethesda, Maryland) was used to calculate their cross-sectional areas. The researcher who performed this assay was blinded to the pain site and to other related information of participants ( Tsai et al, 2021 ).…”

A Deep Learning-Based Quantitative Structure–Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance

“…However, since the image will be similar to the original image, the risk of overfitting, i.e., a decrease in the performance on the test dataset due to the prediction model fitting to match into the training dataset, cannot be ruled [57][58][59][60][61][62][63][64][65][66][67][68]. Thus, data augmentation effectively enables learning with a small number of data.…”