审查用于诊断COVID-19的深度学习方法,其变体和协同药物组合
Qandeel Rafique1, Ali Rehman2, Muhammad Sher Afghan3
1Department of Internal Medicine, Sahiwal Medical College, Sahiwal, 57040, Pakistan.
Computers in biology and medicine
|June 24, 2023
概括
深度学习 (DL) 为COVID-19检测和变种分析提供了一种强大,自动化的方法. 这种方法减少了诊断时间,成本和复杂性,有助于流行病管理.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 随着COVID-19的流行,人们迫切需要快速而准确的诊断工具.
- 现有的诊断方法在速度,成本和可扩展性方面面临着广泛查的挑战.
- 新型冠状病毒变种的出现需要适应性检测策略.
研究的目的:
- 探索开发深度学习 (DL) 驱动的,用于COVID-19检测的全自动框架.
- 为了比较COVID-19检测的各种验证的诊断技术.
- 审查用于COVID-19治疗的协同药物组合,并将其与DL策略整合起来.
主要方法:
- 探索和比较已建立的COVID-19诊断技术.
- 对用于COVID-19治疗的协同药物组合的审查.
- 开发和应用深度学习模型,用于自动检测和变种分析.
- 使用公共数据集和优化DL规则进行分析.
主要成果:
- 深度学习方法显示出有效的COVID-19查的巨大潜力.
- 自动化DL框架可以有效地分析各种冠状病毒变体.
- 协同药物组合提供潜在的治疗途径,以DL洞察为指导.
结论:
- 深度学习提供了一种有价值的工具,可以减少COVID-19诊断的时间,成本和复杂性.
- 将DL与诊断方法相结合,可以提高流行病控制的准确性和效率.
- 这项研究为临床和研究环境提供了关于利用DL用于COVID-19检测和治疗策略的指导.
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