基于消费电子产品的智能技术,用于增强的太赫兹医疗保健,将分割学习与医学成像集成在一起
Sambit Satpathy1, Osamah Ibrahim Khalaf2, Dhirendra Kumar Shukla3
1CSE, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India. sambitmails@gmail.com.
Scientific reports
|May 6, 2024
概括
这项研究引入了一种新的混合算法用于乳腺癌检测,使用特拉赫兹 (THz) 成像和分割学习,达到97.5%的准确性. 该方法增强了成像深度和组织对比度,以便更早,更精确地诊断疾病.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 早期和准确的乳腺癌检测对于有效的治疗至关重要.
- 目前的成像技术在深度和对比度方面存在局限性.
- 在分析过程中保护敏感的患者数据是一个重大挑战.
研究的目的:
- 为增强乳腺癌检测开发一个优化的混合算法.
- 为了提高成像深度和组织对比度,使用特拉赫兹 (THz) 成像.
- 通过贝叶斯式方法与分割学习来确保患者数据隐私.
主要方法:
- 消费电子产品,可穿戴设备和物联网 (IoT) 的整合,用于智能数据收集.
- 使用预期最大化 (EM) 算法进行模型训练.
- 实施一种新的分割学习方法,结合贝叶斯的隐私保护方法.
主要成果:
- 混合算法在乳腺癌检测方面实现了97.5%的高精度.
- 该模型在100个时代内达到最高性能.
- 这与需要更多时代 (例如165年) 的旧型号相比,是一个显著的改进.
结论:
- 拟议的混合算法为乳腺癌检测技术提供了有前途的进步.
- 增强的成像功能和强大的数据隐私是关键的好处.
- 这种方法有助于更早,更准确的诊断,并有可能改善患者的治疗结果.
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