相关实验视频

通过CBDA-ResNet50进行类平衡和数据增强来提高中风风险预测.

Muhammad Asim Saleem1, Ashir Javeed2, Wasan Akarathanawat3,4

  • 1Department of Electrical Engineering, Faculty of Engineering, Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Chulalongkorn University, Bangkok, 10330, Thailand.

Scientific reports
|July 8, 2025
PubMed
概括

一个新的深度学习模型,CBDA-ResNet50,提高了中风风险预测的准确性. 这种先进的方法改善了早期检测和预防,可能降低医疗保健成本.

关键词:
班级平衡是为了平衡.数据增强数据增强深度学习是一种深度学习.在ResNet50中使用ResNet50预测中风的预测

相关概念视频

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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