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相关概念视频

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

470
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
470
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

480
The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
480
Classification of Illness01:17

Classification of Illness

7.6K
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...
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相关实验视频

Updated: Jul 28, 2025

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
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An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

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使用拉曼光谱数据进行疾病分类的多尺度顺序特征选择.

Yue Wei1, Hechang Chen1, Bo Yu2

  • 1School of Artificial Intelligence, Jilin University, Changchun, 130015, China; Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China.

Computers in biology and medicine
|June 2, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多尺度特征选择方法,用于拉曼光谱 (RS) 疾病诊断. 该方法通过整合全球光谱模式和关键的本地信号峰值来提高分类准确性.

关键词:
注意力机制注意力机制疾病的分类疾病的分类.长期短期内存网络中的长期内存.拉曼光谱法 拉曼光谱法 拉曼光谱法

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
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Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

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科学领域:

  • 生物医学光学 生物医学光学
  • 频谱学是一种光谱学.
  • 机器学习用于医疗保健

背景情况:

  • 拉曼光谱 (RS) 提供非破坏性,快速的疾病诊断.
  • 目前的RS方法难以在不同尺度上识别显著信号,从而限制了临床性能.

研究的目的:

  • 利用拉曼光谱学数据开发一种多尺度的顺序特征选择方法,以改进疾病分类.
  • 加强全球光谱特征和区分疾病的关键局部峰值特征的提取.

主要方法:

  • 利用长短期记忆 (LSTM) 网络捕获拉曼光谱内的全球序列特征.
  • 采用注意力机制来识别和选择疾病区分的关键局部峰值特征.

主要成果:

  • 与拉曼光谱分类中最先进的方法相比,拟议的模型表现出更高的性能.
  • 实现了高准确度:COVID-19的97.9 ± 0.2%,H-IV的76.3 ± 0.4%,H-V数据集的96.8 ± 1.9%.

结论:

  • 多尺度顺序特征选择方法有效地整合了全球和本地光谱信息,以准确地分类疾病.
  • 这种方法显著提高了拉曼光谱在医学诊断中的临床适用性.