基于ResNet-EfficientNet的框架,用于基于咳的高精度传染病分类
Dhana Sony Johnson1, Paramasivam Alagumariappan2, Malathy Sathyamoorthy3
1Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
Scientific reports
|November 6, 2025
概括
一个新的深度学习框架有效地使用咳声音对COVID-19进行分类. 该ResNet模型实现了98.5%的准确性,使得早期诊断和减少了SARS-CoV-2病毒的传播.
科学领域:
- * 医学诊断 医学诊断 医学诊断
- * 医疗保健中的人工智能
- * 呼吸系统疾病
背景情况:
- *由SARS-CoV-2引起的COVID-19是一种高度传染性的呼吸道疾病,于2019年底出现,造成全球健康和经济挑战.
- * 快速诊断COVID-19对于控制传播和管理患者护理至关重要,类似于其他传染病.
- * 传播是通过呼吸道滴滴发生的,强调了需要有效的诊断工具的需要.
研究的目的:
- * 提出一种深度学习框架,使用咳声音对COVID-19进行分类.
- *通过咳分析评估各种深度学习模型的性能,包括1D-CNN,DS-CNN,EfficientNet v2和ResNet,用于通过咳分析检测COVID-19.
- * 根据咳声学确定最有效的模型,以准确和快速的COVID-19诊断.
主要方法:
- * 开发一个深度学习框架来分析咳声音.
- * 多种深度学习架构的实施和比较:1D-CNN,DS-CNN,EfficientNet v2和ResNet.
- *使用关键指标进行绩效评估:准确性,精度,回忆,F1-Score,马修斯相关系数 (MCC) 和假阳性率 (FPR).
主要成果:
- * 预先训练的模型,特别是EfficientNet v2和ResNet,与其他深度学习模型相比,表现优越.
- *ResNet模型取得了异常的结果,准确率为98.5%,精度为98.99%,回忆率为98%,F1-Score为0.9849%,MCC为0.9699和FPR为0.01.
- *这些发现表明ResNet在分类COVID-19阳性咳声音方面的优势.
结论:
- * 拟议的深度学习框架,特别是使用ResNet模型,通过咳声音为COVID-19分类提供了一个高度准确的方法.
- *这种方法促进了早期干预,使医生能够及时隔离或治疗患者,并减少疾病传播.
- *使用先进的人工智能进行咳声音分析,为广泛的COVID-19查和管理提供了一个有希望的,非侵入性的工具.
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