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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.
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Acute illness is severe...
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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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CovC-ReDRNet:一种用于COVID-19分类的深度学习模型.

Hanruo Zhu1, Ziquan Zhu1, Shuihua Wang1

  • 1School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK.

Machine learning and knowledge extraction
|April 1, 2024
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概括

一个新的CovC-ReDRNet模型使用深度学习准确区分COVID-19和肺炎. 这种先进的计算机辅助诊断工具为疾病分类和预测提供了高速度和准确性.

关键词:
在COVID-19感染的情况.卷积神经网络是一种卷积神经网络.深度随机向量函数连接连接的深度随机向量函数图像的分类图像的分类.非COVID-19肺炎患者的肺炎患者随机化的神经网络是随机化的神经网络.

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科学领域:

  • 计算机科学 计算机科学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 在全球范围内,COVID-19的流行病已经造成数百万人的死亡.
  • 准确诊断COVID-19至关重要,但由于与肺炎的临床相似性,具有挑战性.
  • 反转录-聚合酶链反应 (RT-PCR) 试验准确,但临床误诊仍然存在.

研究的目的:

  • 开发一种新的深度学习模型,CovC-ReDRNet,用于区分COVID-19患者与肺炎和正常病例.
  • 通过先进的计算机辅助诊断方法,提高COVID-19诊断的准确性和效率.

主要方法:

  • 开发了CovC-ReDRNet模型,使用ResNet-18作为特征表示的支柱.
  • 采用基于特征的随机神经网络 (RNN) 框架,将深度随机向量函数链接网络 (dRVFL) 集成为分类器.
  • 使用五倍交叉验证验证模型.

主要成果:

  • 该CovC-ReDRNet模型实现了高性能指标:94.94%的灵敏度,97.01%的特异性,97.56%的准确性,96.81%的精度,95.84%的F1分数.
  • 废弃研究证实了ResNet-18骨干,RNN与传统分类器的优势,深度RNN与浅层的优势.
  • 该模型超越了最先进的方法,达到97.56%的最大精度,而之前的最佳精度为95.57%.

结论:

  • 该CovC-ReDRNet模型显示出作为COVID-19的先进计算机辅助诊断工具的巨大潜力.
  • 该模型在分类和预测COVID-19时提供了高速和准确的速度,有助于临床决策.
  • 这种深度学习方法解决了COVID-19和肺炎之间的临床误诊的挑战.