区块链支持的医疗监测系统用于早期发现麻疹
Aditya Gupta1, Monu Bhagat1, Vibha Jain1
1Manipal University Jaipur, Jaipur, India.
The Journal of supercomputing
|June 26, 2023
概括
这项研究引入了一个区块链框架,用于使用机器学习早期检测麻疹. 该模型实现了98.80%的准确性,提供了一种安全的疾病识别和分类方法.
科学领域:
- 医疗信息学 医疗信息学
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 麻疹是一个重大的全球健康问题,需要先进的诊断工具.
- 机器学习在癌症等疾病的医学图像分析方面表现有前途.
- 在医疗保健系统中,安全共享健康数据仍然是一个挑战.
研究的目的:
- 提出一个区块链支持的框架,用于早期发现和分类麻疹.
- 为了提高诊断准确度,利用转移学习.
- 为了应对安全的健康信息共享的挑战.
主要方法:
- 开发了一个利用区块链和转移学习的概念框架.
- 该模型在Python 3.9中使用1905年麻疹图像的数据集实现.
- 使用准确度,回忆度,精度和F1得分来评估性能,比较Xception,VGG19和VGG16模型.
主要成果:
- 拟议的框架实现了对天花检测的98.80%的分类准确性.
- 该方法有效地从图像数据中识别和分类麻疹病例.
- 对比分析验证了拟议的方法与已建立的转移学习模型相比.
结论:
- 区块链支持的框架为早期发现麻疹提供了有效和安全的解决方案.
- 转移学习模型在从皮肤病变图像中分类麻疹方面表现出很高的有效性.
- 未来的工作包括扩展模型来诊断其他皮肤疾病,如麻疹和水.
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