AUTHOR=Xiao Duo , Zhu Fei , Jiang Jian , Niu Xiaoqiang 

TITLE=Leveraging natural cognitive systems in conjunction with ResNet50-BiGRU model and attention mechanism for enhanced medical image analysis and sports injury prediction

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1273931

DOI=10.3389/fnins.2023.1273931

ISSN=1662-453X

ABSTRACT=We investigate the potential of integrating natural and artificial cognitive systems in the fields of medical image analysis and sports injury prediction. Analyzing medical images of athletes can offer valuable insights into their health status. We employ the ResNet50-BiGRU model and introduce an attention mechanism, aiming to synergize the strengths of both natural cognitive systems (medical professionals' expertise) and artificial cognitive systems (deep learning models) to enhance the performance of medical image feature extraction and motion injury prediction.Through this integrated approach, we achieve precise identification of anomalies in medical images, such as muscle or bone damage. The evaluation of our method on four medical image datasets, specifically related to skeletal and muscle injuries, yields compelling results. We completed experiments using indicators such as Peak Signal-to-Noise Ratio and Structural Similarity Index, which confirmed the effectiveness of our method in sports injury analysis.The significance of our research lies in providing an effective deep learning-driven method that harnesses the potential of both natural and artificial cognitive systems. By combining human expertise and advanced machine learning techniques, we contribute to a comprehensive understanding of athletes' health status and hold potential implications for enhancing sports injury prevention. Our study seeks to advance the field of medical image and signal processing, paving the way for improved diagnostic accuracy and personalized treatment plans for athletes, ultimately promoting better overall health and performance outcomes. Despite the advancements in medical image analysis and sports injury prediction, the existing systems often face challenges in accurately identifying subtle anomalies and providing precise injury risk assessments, highlighting the need for a more integrative and comprehensive approach.