A gated convolutional neural network for classification of breast lesions in ultrasound images

Effect of Rehabilitation Physical Training on Basketball Injury under Ultrasound Examination

In order to solve the problem of observing the effect of rehabilitation physical training on basketball player injury, a kind of observation study on the effect of rehabilitation physical training on basketball player injury based on memory ultrasound examination was proposed. This study makes a comprehensive analysis of physical fitness training factors such as physical fitness test plan, training arrangement, training method, training monitoring, and effect evaluation. Through the experiment, it is found that there are still gaps in physical training, diagnostic standards, training plans and requirements, load monitoring, standardization of operation, nutrition, and recovery in the training organization. There are only 23.81% of full-time physical coaches and 25.40% of professional team doctors at all levels of sports teams. There were only 23.81% of full-time physical fitness coaches and 25.40% of professional team doctors in all levels of sports teams. Basketball players in the basketball injury survey had 67 sports injuries throughout the year, with a rate of 3.35 injuries per person and 2.48 injuries per 1000 hours. The location of chronic injuries is usually the lower back, knee joint, and ankle. The main injuries are due to the physical component, with injuries occurring most frequently throughout the training season in July, August, and September. Experiments show that good and professional physical training can reduce the risk of injury to a greater extent than strengthening the specific skills of athletes.

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.