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ODQN-Net:优化深度Q神经网络,通过舌头图像分析进行疾病预测,使用Remora优化算法.

S V N Sreenivasu1, P Santosh Kumar Patra2, Vasujadevi Midasala3

  • 1Department of Computer Science and Engineering, Narasaraopeta Engineering College (A), Narasaraopet, India.

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概括
此摘要是机器生成的。

这项研究引入了一个优化的深度Q神经网络 (ODQN-Net) 来从舌头图像中准确预测疾病,改进了传统方法. 通过使用增强的图像处理和特征提取技术,ODQN-Net在分类多种疾病方面实现了高准确性.

关键词:
印度的阿育吠陀医学是印度的医药.雷莫拉优化算法 雷莫拉优化算法深度 Q-神经网络的神经网络当地的三元格模式.语言图像分析语言图像分析

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科学领域:

  • 人工智能的人工智能
  • 医疗成像医学成像
  • 传统印度医学 传统印度医学

背景情况:

  • 传统的阿育吠陀医学依赖于手动舌头分析来诊断疾病,这耗时且缺乏精确性.
  • 现有的基于舌头的疾病预测机器学习模型没有达到足够的准确性,特别是对于多类分类.
  • 从舌头图像中准确和自动识别疾病仍然是一个重大挑战.

研究的目的:

  • 开发一个优化的深度Q神经网络 (ODQN-Net) 来增强从舌头图像的疾病识别和分类.
  • 与现有方法相比,提高疾病预测的准确性和效率.
  • 为了解决手动诊断和目前在阿育吠陀医学中的AI方法的局限性.

主要方法:

  • 使用多度 retinex 方法进行图像增强,以提高质量和降低噪音.
  • 使用局部三元模式进行基于颜色的分析和Remora优化算法进行高效选择的特征提取.
  • 使用优化深度Q神经网络 (ODQN-Net) 模型对疾病进行分类.

主要成果:

  • 拟议的ODQN-Net在舌头成像数据集上实现了99.17%的高精度.
  • 记录了出色的性能指标,包括99.75%的F1得分和99.84%的马修相关系数.
  • 与当前最先进的方法相比,ODQN-Net表现出卓越的性能.

结论:

  • ODQN-Net模型提供了一个高度准确和高效的解决方案,用于自动预测和分类从舌头图像疾病.
  • 这种人工智能驱动的方法有可能通过克服手工检查的局限性来彻底改变阿育吠陀诊断.
  • 这项研究强调了将高级深度学习与优化特征提取用于医学图像分析的有效性.