基于云计算的深度学习范式的阿尔茨海默病检测
Dayananda Pruthviraja1, Sowmyarani C Nagaraju2, Niranjanamurthy Mudligiriyappa3
1Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, India.
Diagnostics (Basel, Switzerland)
|August 26, 2023
概括
深度学习模型使用MRI扫描准确地分类阿尔茨海默病 (AD) 阶段. 一个基于云的新型应用程序实现了98%的准确性,帮助远程诊断.
科学领域:
- 人工智能的人工智能
- 医学成像分析 医学成像分析
- 神经学 神经学
背景情况:
- 深度学习在医学图像分析的复杂模式识别方面表现出色.
- 准确的阿尔茨海默病 (AD) 分类对于及时干预至关重要.
- 传统的算法在分类复杂的神经退行性模式方面面临着挑战.
研究的目的:
- 开发一个本地基于云的深度学习解决方案,用于多类阿尔茨海默病 (AD) 分类.
- 通过预训练的GoogleLeNet模型利用转移学习来提高AD诊断.
- 使用MRI扫描创建一个用户友好的Web应用程序,用于使用MRI扫描进行远程AD预测.
主要方法:
- 使用预训练的googlenet模型进行图像分类.
- 雇员转移学习,以微调谷歌网络模型的AD分类.
- 开发了一个本地基于云的Web应用程序,集成了微调的模型.
- 输入方式:磁共振成像 (MRI) 扫描.
- 分类:多类为AD的四个阶段.
主要成果:
- 达到98%的高分类准确度.
- 成功开发了一个功能性的本地云网络应用程序,用于阿尔茨海默病的预测.
- 证明了转移学习在提高AD分类准确性的有效性.
结论:
- 提出的深度学习方法,利用google.net和转移学习,为阿尔茨海默病的分类提供了一个非常准确的方法.
- 开发的基于云的应用程序为医疗专业人员提供了远程和高效的AD诊断.
- 这项技术有可能显著改善阿尔茨海默病的早期检测和管理.
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