焦点Covid:使用深度学习与胸部X射线图像的自动COVID-19检测
Tarun Agrawal1, Prakash Choudhary1
1Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India.
Evolving systems
|April 16, 2024
概括
这项研究提出了一个深度卷积神经网络,用于使用胸部X射线检测2019年冠状病毒病 (COVID-19). 人工智能模型显示高准确度,有助于早期查和减少医疗保健工作量.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染性疾病 传染性疾病
背景情况:
- 冠状病毒疾病2019 (COVID-19),最初是2019-nCoV,后来是SARS-CoV-2,在2020年1月被世界卫生组织宣布为大流行.
- 随着COVID-19病例数量的不断增加,医疗基础设施面临压力,需要高效的诊断工具.
- 医学成像,包括计算机断层扫描 (CT) 和胸部放射,对于早期查和检测COVID-19至关重要.
研究的目的:
- 为自动化COVID-19检测提出一个深层卷积神经网络 (CNN) 架构.
- 利用胸部放射图进行准确和快速的COVID-19病例识别.
- 探索人工智能 (AI) 在减轻大流行期间医疗保健系统负担中的作用.
主要方法:
- 基于深度卷积神经网络模型的开发.
- 训练和测试模型,使用来自公共存储库的胸部X射线图数据集.
- 评估模型在检测COVID-19中的表现.
主要成果:
- 拟议的深层卷积神经网络从胸部放射图中检测到COVID-19的高精度.
- 人工智能模型展示了有效查和早期病例识别的潜力.
- 这项研究强调了人工智能在支持传染病医学成像诊断方面的实用性.
结论:
- 深度学习模型,特别是CNN,显示出使用胸部X射线图检测COVID-19的重大前景.
- 人工智能辅助诊断可以提高医疗基础设施在管理公共卫生危机方面的效率.
- 关于COVID-19诊断的临床决定必须与经过培训的医疗专业人员协商.
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