2020
DOI: 10.1007/978-3-030-51549-2_8
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A Convolutional Neural Network Model to Classify the Effects of Vibrations on Biceps Muscles

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Cited by 4 publications
(5 citation statements)
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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%
“…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%
“…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%
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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%