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相关概念视频

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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使用混合特征工程机制有效的多类肺病分类.

Binju Saju1, Neethu Tressa1, Rajesh Kumar Dhanaraj2

  • 1Department of Master of Computer Applications, New Horizon College of Engineering, Bengaluru, India.

Mathematical biosciences and engineering : MBE
|December 5, 2023
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概括

一个新的混合深度学习模型从胸部X射线准确地分类了13种肺部疾病. 这种先进的计算机辅助诊断系统达到97.00%的准确性,帮助医疗专业人员快速准确地检测肺部疾病.

关键词:
阿奎拉优化器是Aquila优化器.聪明的边缘检测检测器这就是DENSENET121的意义.批量均等化 批量均等化胸部X射线 胸部X射线对比度有限的适应性直方体平衡等同化.肺部疾病 肺部疾病这就是Otsu.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 肺部病理学 肺部病理学

背景情况:

  • 随着COVID-19的爆发,人们越来越需要先进的肺部疾病诊断.
  • 计算模型对于医学图像分类和支持临床决策越来越重要.
  • 通过胸部X射线影像对各种肺部病理的准确和快速诊断仍然是一个重大挑战.

研究的目的:

  • 引入一种先进的计算机辅助模型,用于对胸部X射线图像进行深度学习,对13种不同的肺部疾病进行分类.
  • 提高图像质量和提取相关特征,以提高诊断准确度.
  • 提出和评估一种新的混合深度学习模型,用于肺部疾病的分类.

主要方法:

  • 利用了112,000张胸部X射线图像的开源数据集.
  • 应用了预处理技术,包括基于Otsu的二进制转换,自适应式直方图等级,以及用于图像增强的Canny边缘检测.
  • 采用特征提取方法 (连接区域,HOG,GLCM,Haar波束) 和选择 (RCNA),提出了一个优化的混合模型,将卷积神经网络 (CNN) 和DENSENET121与Aquila优化和批量均等结合起来.

主要成果:

  • 与独立的CNN和DENSENET121.1.相比,拟的混合型实现了更高的性能.
  • 获得了97.00%的准确性,94.00%的精度,96.00%的敏感性,96.00%的特异性和95.00%的F1评分,用于分类13种肺部疾病.
  • 在肺部疾病的医学图像分类中证明了优化混合方法的有效性.

结论:

  • 开发的优化混合深度学习模型显著提升了从胸部X射线图准确分类肺部疾病的能力.
  • 该模型的高性能指标表明它有可能成为医疗专业人员在诊断肺部疾病方面的宝贵工具.
  • 未来的工作可能涉及整合可解释的AI和进一步的模型优化,以提高临床效用.