scholarly journals Relationship between Volleyball Sports Nutrition Food and Sports Athletes’ Training and Physical Health Based on Medical Image Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jian Liu
Keyword(s):  
Energy Consumption ◽  
Physical Health ◽  
Image Recognition ◽  
Medical Image ◽  
Intervention Group ◽  
Great Influence ◽  
Nutritious Food ◽  
Volleyball Players ◽  
Medical Image Recognition ◽  
The Impact

As we all know, the dietary nutrition of athletes has a great influence on physical condition and exercise ability. A good diet pattern is the basis of a reasonable diet for athletes. It helps to improve the function and physical state of athletes. This article is aimed at studying the impact of nutritious food on athletes’ training and physical health. This article proposes the relevant technology of medical image recognition, which is used to study the relationship between nutritious food and the health of volleyball players and athletes, and proposes methods such as weighing method, meal review method, and measurement method, and the purpose is to exercise nutritional research and provide new ideas and methods. In addition, 200 female volleyball players were randomly selected for comparative analysis. The experimental results in this paper show that the energy intake and energy consumption of the female intervention group maintained a balance after the intervention, and there was a significant change in the negative balance state before the intervention. The energy consumption changed from − 158.2 ± 156.2 to − 157.2 ± 129.6 . The number of athletes whose weight is close to the ideal range has increased from 44.8% to 48.5%.

Bayesian belief networks for medical image recognition

1993 ◽  
Author(s):  
Chien-Shung Hwang ◽  
Wei-Chung Lin ◽  
Chin-Tu Chen ◽  
Shiuh-Yung J. Chen
Keyword(s):  
Image Recognition ◽  
Medical Image ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
Medical Image Recognition

scholarly journals An Improved VGG16 Model for Pneumonia Image Classification

2021 ◽  
Vol 11 (23) ◽  
pp. 11185
Author(s):  
Zhi-Peng Jiang ◽  
Yi-Yang Liu ◽  
Zhen-En Shao ◽  
Ko-Wei Huang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Medical Image ◽  
Best Practice ◽  
Data Augmentation ◽  
X Rays ◽  
Neural Network Models ◽  
X Ray ◽  
Medical Image Recognition

Image recognition has been applied to many fields, but it is relatively rarely applied to medical images. Recent significant deep learning progress for image recognition has raised strong research interest in medical image recognition. First of all, we found the prediction result using the VGG16 model on failed pneumonia X-ray images. Thus, this paper proposes IVGG13 (Improved Visual Geometry Group-13), a modified VGG16 model for classification pneumonia X-rays images. Open-source thoracic X-ray images acquired from the Kaggle platform were employed for pneumonia recognition, but only a few data were obtained, and datasets were unbalanced after classification, either of which can result in extremely poor recognition from trained neural network models. Therefore, we applied augmentation pre-processing to compensate for low data volume and poorly balanced datasets. The original datasets without data augmentation were trained using the proposed and some well-known convolutional neural networks, such as LeNet AlexNet, GoogLeNet and VGG16. In the experimental results, the recognition rates and other evaluation criteria, such as precision, recall and f-measure, were evaluated for each model. This process was repeated for augmented and balanced datasets, with greatly improved metrics such as precision, recall and F1-measure. The proposed IVGG13 model produced superior outcomes with the F1-measure compared with the current best practice convolutional neural networks for medical image recognition, confirming data augmentation effectively improved model accuracy.

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