An improved AlexNet model for automated skeletal maturity assessment using hand X-ray images

He, Mingguang; Zhao, Xudong; Yu, Lu; Hu, Yuze

doi:10.1016/j.future.2021.03.018

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…It can actively build an information management platform for athlete training and competition, which is conducive to the big data integration of valuable data information such as functional consumption, technical tracking, and tactical analysis. In the way of mobile data transmission, the coach can realize the visualization of the adjustment of the on-site athletes' functional level and the adjustment of technical and tactics, which can form a complete information of the athletes' training, and competition, which is strongly supported by science and technology [10][11][12].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Signal Recognition Based on APSO‐RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

Guo

Huang

2021

Computational Intelligence and Neuroscience

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The advanced analysis and research methods of big data will provide theoretical support for the integration of athletes’ talent training. The advanced technological methods of big data will also give full play to the advantages of tapping the potential of talents and actively improve the success rate of grassroots young athletes. This paper proposes an improved Adaptive Particle Swarm Optimization (APSO) algorithm for the optimization of radial basis function (RBF) neural network parameters. The basic structure of RBF neural network is introduced, and the influence of parameters on the performance of RBF neural network is analyzed. The optimization method of RBF neural network parameters is analyzed, and Particle Swarm Optimization (PSO) algorithm is selected as the parameter optimization method of RBF neural network. In addition, an improved APSO algorithm is proposed according to the advantages and disadvantages of PSO and compared with other PSO algorithms. Experimental results show that the improved PSO algorithm has better accuracy. The improved PSO algorithm is applied to the parameter optimization of RBF neural network, and the experimental results prove the superiority of the proposed method. By weighting the second-level indicators, the weights of the second-level indicators of athletes’ competitive ability are in order of skill, athletic quality, psychological ability, and artistic expression. Skills are the main factors that determine the level of competitive ability. Sports quality and psychological ability are important guarantees for supporting the normal performance of skills. Artistic expressiveness is a supplementary factor for competitive ability. The various elements cooperate with each other and interact with each other. The indicators do not exist alone but cooperate with each other to support the formation of the entire athletic ability system. In the content of the competitive ability index of excellent athletes, technical ability is the core, and sports quality, psychological ability, and artistic performance ability ultimately exist to serve the improvement of technical ability. The competition scores of the 100 athletes counted in this article are all above 9.0 points. The difference between APSO-RBF’s action quality scores of 100 athletes and the real value is less than 3%. In terms of movement difficulty, the APSO-RBF evaluated athletes’ scores are close to 1.65 points, which is basically the same as the real value.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Signal Recognition Based on APSO‐RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

Guo

Huang

2021

Computational Intelligence and Neuroscience

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“…The first considered architecture is Alexnet, which is a relatively simple, 8-layer feed-forward architecture. The benefits of Alexnet are its robustness and computational efficiency-but it often performs more poorly in comparison with more complex networks, especially on higher-resolution images [34,35]. One of the common issues with CNNs is that deeper networks may cause issues with the so-called gradient vanishing [36] This problem can cause the models to be untrainable.…”

Section: Utilized Cnn Architecturesmentioning

confidence: 99%

Quality Assessment Assistance of Lateral Knee X-rays: A Hybrid Convolutional Neural Network Approach

et al. 2023

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A common issue with X-ray examinations (XE) is the erroneous quality classification of the XE, implying that the process needs to be repeated, thus delaying the diagnostic assessment of the XE and increasing the amount of radiation the patient receives. The authors propose a system for automatic quality classification of XE based on convolutional neural networks (CNN) that would simplify this process and significantly decrease erroneous quality classification. The data used for CNN training consist of 4000 knee images obtained via radiography procedure (KXE) in total, with 2000 KXE labeled as acceptable and 2000 as unacceptable. Additionally, half of the KXE belonging to each label are right knees and left knees. Due to the sensitivity to image orientation of some CNNs, three approaches are discussed: (1) Left-right-knee (LRK) classifies XE based just on their label, without taking into consideration their orientation; (2) Orientation discriminator (OD) for the left knee (LK) and right knee (RK) analyses images based on their orientation and inserts them into two separate models regarding orientation; (3) Orientation discriminator combined with knee XRs flipped to the left or right (OD-LFK)/OD-RFK trains the models with all images being horizontally flipped to the same orientation and uses the aforementioned OD to determine whether the image needs to be flipped or not. All the approaches are tested with five CNNs (AlexNet, ResNet50, ResNet101, ResNet152, and Xception), with grid search and k-fold cross-validation. The best results are achieved using the OD-RFK hybrid approach with the Xception network architecture as the classifier and ResNet152 as the OD, with an average AUC of 0.97 (±0.01).

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“…Common convolution neural networks are Le Net [6] , Alex Net [7] , VGG [8][9] and Google Net [10] . VGG Net is invoked as a model developed by the Visual Geometry Group of the University of Oxford.…”

Section: Vgg16 Network Architecturementioning

confidence: 99%

Pavement Recognition Based on Improving VGG16 Network Model

2023

IJNDES

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In order to improve the accuracy of pavement recognition, an improved RA-VGG16 network model classification method based on VGG16 is proposed in this paper. The improvements include reducing the number of convolution cores in VGG16 to optimize the network structure, adding the improved residual attention module to achieve the extraction of road notable features, using the global average pooling layer instead of the full connection layer to significantly reduce the network parameters and prevent network over fitting. The experimental results show that the accuracy of the improved VGG16 network model is 99.36%, 15.26% higher than that of the original VGG16, and significantly higher than other models (KNN, Alex Net, VGG13, ResNet50, ResNet101).

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An improved AlexNet model for automated skeletal maturity assessment using hand X-ray images

Cited by 8 publications

References 25 publications

[Retracted] Signal Recognition Based on APSO‐RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

[Retracted] Signal Recognition Based on APSO‐RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

Quality Assessment Assistance of Lateral Knee X-rays: A Hybrid Convolutional Neural Network Approach

Pavement Recognition Based on Improving VGG16 Network Model

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