改变医疗保健:对深度学习在医学成像学中的作用进行比较的洞察
Vivek Kumar Prasad1, Ashwin Verma1, Pronaya Bhattacharya2
1Department of CSE, Institute of Technology Nirma University, Ahemdabad, Gujarat, India.
Scientific reports
|December 5, 2024
概括
深度学习模型在阿尔茨海默病MRI分析中显示出高准确度. 一个新的云计算框架增强了医疗图像处理和患者数据隐私.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 云计算 云计算 云计算
背景情况:
- 深度学习 (DL) 模型在医学图像分析中表现出高精度.
- 阿尔茨海默病 (AD) 诊断通常依赖于磁共振成像 (MRI) 分析.
- 集成云计算 (CC) 解决了医疗成像DL模型中的计算限制.
研究的目的:
- 系统地探索医学成像数据集分类的教程方法.
- 通过DL方法对阿尔茨海默氏症 (AD) 的MRI分类进行案例研究.
- 引入医疗图像处理的新框架,增强数据隐私.
主要方法:
- 讨论了医学成像数据集的分类分类法.
- 分析了三种不同的DL模型 (CNN,VGG-16,ensemble) 用于ADMRI分类.
- 设计了一个具有输入,基于云的预处理/执行和诊断层的新框架.
主要成果:
- 卷积神经网络 (CNN) 实现了最高的测试准确率,达到99.285%.
- 视觉几何组16 (VGG-16) 实现了85.113%的测试准确度.
- 整体模型实现了79.192%的测试准确性,云框架确保了数据隐私.
结论:
- CNN模型为阿尔茨海默氏症MRI分类提供了卓越的性能.
- 拟议的云计算框架有效地处理医疗图像,同时保持患者的保密性.
- 这项研究为开发疾病特异性DL模型用于医学成像分析提供了宝贵的资源.
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