从使用人工智能驱动的知识蒸的X射线图中检测COVID-19,肺炎和结核病
Md Mohsin Kabir1, M F Mridha2, Ashifur Rahman1
1Department of Computer Science & Engineering, Bangladesh University of Business & Technology, Dhaka-1216, Bangladesh.
Heliyon
|March 6, 2024
概括
这项研究引入了一种简化深度学习模型,用于通过知识蒸从胸部X射线实时检测COVID-19等呼吸系统疾病. 新方法实现了高精度,同时在实际医疗应用中具有计算效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 胸部放射学对于诊断呼吸系统疾病 (COVID-19,肺炎,结核病) 是至关重要的.
- 准确的口译需要专业知识和专门的设备.
- 传统的深度学习模型是复杂的,阻碍了在现实世界应用中部署.
研究的目的:
- 为了减少深度学习模型的复杂性,用于自动化呼吸系统疾病检测.
- 在各种平台上实现实时诊断功能.
- 提高AI在医学成像中的实际应用性.
主要方法:
- 实施知识蒸技术 (KDT),将知识从一个大型网络转移到一个较小的网络.
- 从三个公共数据集的胸部X射线集成数据集上训练模型.
- 专注于降低计算复杂性,同时保持诊断性能.
主要成果:
- 与传统方法相比,知识蒸方法显著降低了模型的复杂性.
- 实现了高性能指标:平均准确率为0.97,精度为0.94,回忆率为0.97.
- 在实时检测呼吸道疾病方面表现出卓越的性能.
结论:
- 知识蒸是创建医疗成像计算效率高的深度学习模型的有效方法.
- 拟议的系统提供了一个实用的解决方案,用于实时检测呼吸系统疾病,使用胸部X射线图.
- 这种方法有助于在不同的医疗保健环境中部署AI驱动的诊断工具.
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