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

Ardhianto, Peter; Tsai, Jen Yung; Lin, Chih-Yang; Liau, Ben-Yi; Jan, Yih‐Kuen; Akbari, Veit Babak Hamun; Lung, Chi-Wen

doi:10.3390/app11094021

Cited by 6 publications

(6 citation statements)

References 68 publications

(105 reference statements)

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“…These pre-trained can contribute to MobileNet's high accuracy in PD classification tasks [41]. Additionally, this work was carried out by highlighting the high classification results achieved by pre-trained CNNs, indicating their effectiveness in disease classification tasks [29].…”

Section: Discussionmentioning

confidence: 91%

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Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images

Afandi,

Ardhianto,

Lin

2023

Preprint

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Neurodegenerative illnesses, such as Parkinson’s disease (PD), have a substantial impact on the overall well-being of those who are affected. This study investigates and contrasts the capabilities of convolutional neural networks (CNN) in detecting Parkinson’s disease (PD) by utilising hand-drawn images alongside wave and spiral images as input data. This study employs pre-trained CNN models, specifically MobileNet, ResNet50, EfficientNet-B1, and InceptionV3, to classify Parkinson’s disease (PD). The findings demonstrate that MobileNet surpasses other architectural designs, as evidenced by the F1-Score of the four classes: Spiral Normal (0.87), Spiral Parkinson (0.86), Wave Normal (0.97), and Wave Parkinson (0.97). MobileNet has also shown a remarkable accuracy of 0.92 in diagnosing Parkinson’s disease. The result demonstrates the efficacy of MobileNet in extracting features from images. The results of this study enhance the application of deep learning methods in the early detection of PD, as well as help indicate the effectiveness of patient therapy and exercise, promising better patient outcomes through timely intervention and treatment.

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Section: Discussionmentioning

confidence: 91%

“…The augmentation technique was used to encounter the minimum data. The image augmentation improves the deep learning model training results [28,29]. Therefore, this study used data augmentation such as rotation 15°, zoom range 0.2, width shift range 0.2, and height shift range 0.2 to simulate real-life situations of hand drawing images.…”

Section: Image Datasetmentioning

confidence: 99%

Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images

Afandi,

Ardhianto,

Lin

2023

Preprint

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“…Average precision can be used as a comprehensive evaluation index to balance the effects of precision and recall and evaluate a model more thoroughly. The precision and recall curve area is the average precision value, and a larger value indicates better model performance [20].…”

Section: Deep Learning Performancementioning

confidence: 99%

“…Deep learning has been used in footprints with image features to auto-detect foot identification [20]. Object detection is a popular deep learning method that trains on images and directly optimizes performance when making predictions [21].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure

et al. 2022

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People with cerebral palsy (CP) suffer primarily from lower-limb impairments. These impairments contribute to the abnormal performance of functional activities and ambulation. Footprints, such as plantar pressure images, are usually used to assess functional performance in people with spastic CP. Detecting left and right feet based on footprints in people with CP is a challenge due to abnormal foot progression angle and abnormal footprint patterns. Identifying left and right foot profiles in people with CP is essential to provide information on the foot orthosis, walking problems, index gait patterns, and determination of the dominant limb. Deep learning with object detection can localize and classify the object more precisely on the abnormal foot progression angle and complex footprints associated with spastic CP. This study proposes a new object detection model to auto-determine left and right footprints. The footprint images successfully represented the left and right feet with high accuracy in object detection. YOLOv4 more successfully detected the left and right feet using footprint images compared to other object detection models. YOLOv4 reached over 99.00% in various metric performances. Furthermore, detection of the right foot (majority of people’s dominant leg) was more accurate than that of the left foot (majority of people’s non-dominant leg) in different object detection models.

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“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Ardhianto

Subiakto

Lin

et al. 2022

Sensors

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Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA.

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

Cited by 6 publications

References 68 publications

Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images

Diagnosis of Parkinson’s Disease Using Convolutional Neural Network by Hand Drawing Images

Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure

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

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