使用扩展卷积神经网络的COVID-19疾病预测基于征收飞行礼服队伍优化
1Computer Science (Tenure Stream), School of Engineering and Computer Science, University of Pittsburgh, Johnstown US.
Wireless personal communications
|June 26, 2023
概括
这项研究引入了一种新的深度学习方法,用于使用CT扫描进行早期COVID-19检测. 这种方法提高了诊断的准确性,有助于在疫情期间及时照顾患者.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- COVID-19 流行病在全球范围内造成了广泛的健康危机.
- 早期诊断COVID-19对于有效治疗和患者的治疗结果至关重要.
- 深度学习在加速诊断程序方面提供了有希望的进展.
研究的目的:
- 提出一种基于深度学习的新技术,用于早期检测COVID-19.
- 通过医学成像来提高COVID-19诊断的准确性.
主要方法:
- 对收集的CT图像应用高斯过器.
- 使用一个状扩展卷积神经网络进行图像分析.
- 优化深度学习的超参数,采用基于飞行的形行为算法.
- 将图像分类为COVID-19阳性或阴性.
主要成果:
- 拟议的深度学习方法在COVID-19诊断研究中表现出卓越的表现.
- 评估指标证实了新方法的有效性.
- 该技术成功地将COVID-19和非COVID-19病例分类,准确度提高.
结论:
- 开发的深度学习技术显示了准确和早期检测COVID-19的巨大潜力.
- 这种方法可以帮助医疗保健专业人员管理大流行.
- 进一步的研究和验证对于临床实施是有必要的.
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