作为服务的医疗保健 (HAAS):基于CNN的云计算模型,可无处不在地访问肺癌诊断
Nuruzzaman Faruqui1,2, Mohammad Abu Yousuf1, Faris A Kateb3
1Institute of Information Technology (IIT), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh.
Heliyon
|November 9, 2023
概括
本研究介绍了使用人工智能进行早期肺癌检测的医疗保健即服务 (HAAS). 哈斯网模型达到96.07%的准确性,提高了可访问性,并可能降低死亡率.
科学领域:
- 医疗成像中的人工智能
- 云计算用于医疗保健
- 瘤学和诊断系统
背景情况:
- 卷积神经网络 (CNN) 分类器通过计算机断层扫描 (CT) 扫描进行了先进的自动肺癌诊断.
- 当前的研究往往忽视了这些诊断技术的可用性和现实应用.
- 需要弥合研究成果与实践临床实施之间的差距,以早期检测肺癌.
研究的目的:
- 推出医疗保健即服务 (HAAS),这是一个基于云计算的肺癌诊断系统.
- 提高高级AI肺癌诊断工具的可访问性和可用性.
- 通过早期和广泛的诊断来降低肺癌死亡率.
主要方法:
- 开发HAASNet,一个云兼容的卷积神经网络 (CNN) 分类器.
- 整合HAASNet预测与来自医疗物联网 (IoMT) 的身体症状数据.
- 利用云计算和IoMT为全球可访问的,基于互联网的诊断服务.
主要成果:
- 哈斯网的诊断准确率达到了96.07%.
- 综合HAAS模型表现出高性能,平均精度 (96.47%),回忆 (95.39%) 和F1得分 (94.81%).
- 该系统提供准确可靠的肺癌诊断报告.
结论:
- 拟议的HAAS模型,整合HAASNet和IoMT,为早期肺癌诊断提供了一个可行的解决方案.
- 云和IoMT技术使全球可访问的服务,超越地理限制.
- 这种方法有可能通过为更广泛的人群提供早期诊断来显著降低肺癌死亡率.
关键词:
在美国,CNN是CNN.这就是为什么CTCTCTCTCT云计算是一种云计算.计算机断层扫描图像的图像.卷积神经网络是一种卷积神经网络.互联网的医疗东西的互联网.一个小小的东西.肺癌的分类 肺癌的分类优化算法的优化算法更多相关视频
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