A Convolutional Neural Network Model to Classify the Effects of Vibrations on Biceps Muscles

Tsai, Jen Yung; Jan, Yih‐Kuen; Liau, Ben-Yi; Subiakto, Raden Bagus Reinaldy; Lin, Chen‐Yuan; Hendradi, Rimuljo; Hsu, Yi Chuan; Lin, Quanxin; Chang, Hsin Ting; Lung, Chi-Wen

doi:10.1007/978-3-030-51549-2_8

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…The deep learning method is applied to high-volume and repeatable processes to recognize biomedical images. Deep learning methods use advanced graphic processing units (GPUs) to calculate the featured figures and auto-classify or identify the image’s objects ( Tsai et al, 2020 ). At the moment, there is a shortage of confirmed diagnosis MRI images for the lumbar vertebrae dataset.…”

Section: Introductionmentioning

confidence: 99%

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

Tsai

Hung

Guo

et al. 2021

Front. Bioeng. Biotechnol.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Tsai

Hung

Guo

et al. 2021

Front. Bioeng. Biotechnol.

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“…In contrast, with deep learning classification of bicep vibration, the cropping strategy was not used because it requires an entire image to identify the difference in muscle thickness on the bicep vibration. AlexNet provided 82.50% of the results and is comparable with VGG-16 and VGG-19 for the test results with the same number of images [32].…”

Section: Classification Architecture In Skeletal Musclementioning

confidence: 74%

“…Meanwhile, the RAN was the latest development of the region-based convolutional neural network (RCNN) [36]. The classification entirely uses a CNN based on AlexNet, Deep-CNN, and VGG [30][31][32] (Figure 4).…”

Section: Network Architecturementioning

confidence: 99%

“…In 2018 (Figure 1), deep learning was increasingly used to support skeletal muscle and smooth muscle ultrasound imaging in order to improve reliability testing for the classification of muscle types [30], classifying muscles by gender [31], and the classification of muscle vibration [32]. Additionally, it supported the identification of landmarks such as segmentation in the orientation of muscle fibers [33] and tracking the cross-sectional area of the rectus femoris [34].…”

Section: Introductionmentioning

confidence: 99%

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A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images

et al. 2021

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Deep learning has aided in the improvement of diagnosis identification, evaluation, and the interpretation of muscle ultrasound images, which may benefit clinical personnel. Muscle ultrasound images presents challenges such as low image quality due to noise, insufficient data, and different characteristics between skeletal and smooth muscles that can affect the effectiveness of deep learning results. From 2018 to 2020, deep learning has the improved solutions used to overcome these challenges; however, deep learning solutions for ultrasound images have not been compared to the conditions and strategies used to comprehend the current state of knowledge for handling skeletal and smooth muscle ultrasound images. This study aims to look at the challenges and trends of deep learning performance, especially in regard to overcoming muscle ultrasound image problems such as low image quality, muscle movement in skeletal muscles, and muscle thickness in smooth muscles. Skeletal muscle segmentation presents difficulties due to the regular movement of muscles and resulting noise, recording data through skipped connections, and modified layers required for upsampling. In skeletal muscle classification, the problems faced are area-specific, thus making a cropping strategy useful. Furthermore, there is no need to add additional layer modifications for smooth muscle segmentation as muscle thickness is the main problem in such cases.

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“…Our theory is supported by the correlation analysis, which revealed that an increased rotation angle leads to a narrower vertical acromiohumeral distance. Furthermore, our techniques can be incorporated with artificial intelligence ( Cheng and Malhi, 2017 ; Tsai et al, 2020 ) in the future to depict the sub-acromial motion in a speedy manner.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Analysis of Dynamic Subacromial Ultrasonography: Reliability and Influencing Factors

Lin

Chou

Chen

et al. 2022

Front. Bioeng. Biotechnol.

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Objective: Current imaging methods used to examine patients with subacromial impingement syndrome (SIS) are limited by their semi-quantitative nature and their capability of capturing dynamic movements. This study aimed to develop a quantitative analytic model to assess subacromial motions using dynamic ultrasound and to examine their reliability and potential influencing factors.Method: We included 48 healthy volunteers and examined their subacromial motions with dynamic ultrasound imaging. The parameters were the minimal vertical acromiohumeral distance, rotation radius, and degrees of the humeral head. The generalized estimating equation (GEE) was used to investigate the impact of different shoulder laterality, postures, and motion phases on the outcome.Result: Using the data of the minimal vertical acromiohumeral distance, the intra-rater and inter-rater reliabilities (intra-class correlation coefficient) were determined as 0.94 and 0.88, respectively. In the GEE analysis, a decrease in the minimal vertical acromiohumeral distance was associated with the abduction phase and full-can posture, with a beta coefficient of −0.02 cm [95% confidence interval (CI), −0.03 to −0.01] and −0.07 cm (95% CI, −0.11 to −0.02), respectively. The abduction phase led to a decrease in the radius of humeral rotation and an increase in the angle of humeral rotation, with a beta coefficient of −1.28 cm (95% CI, −2.16 to −0.40) and 6.60° (95% CI, 3.54–9.67), respectively. A significant negative correlation was observed between the rotation angle and radius of the humeral head and between the rotation angle and the minimal vertical acromiohumeral distance.Conclusion: Quantitative analysis of dynamic ultrasound imaging enables the delineation of subacromial motion with good reliability. The vertical acromiohumeral distance is the lowest in the abduction phase and full-can posture, and the rotation angle of the humeral head has the potential to serve as a new parameter for the evaluation of SIS.

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A Convolutional Neural Network Model to Classify the Effects of Vibrations on Biceps Muscles

Cited by 4 publications

References 14 publications

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

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

A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images

Quantitative Analysis of Dynamic Subacromial Ultrasonography: Reliability and Influencing Factors

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