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X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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相关实验视频

Updated: Jun 19, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

基于X射线图像的实时COVID-19诊断使用深度神经网络 (CXR-DNNs)

Ali Yousuf Khan¹, Miguel-Angel Luque-Nieto², Muhammad Imran Saleem³

¹Telecommunications Engineering School, University of Malaga, 29010 Malaga, Spain.

Journal of imaging

|December 27, 2024

概括

这项研究开发了一种使用胸部X射线检测COVID-19感染的AI工具. 新方法的准确性达到了95%,改善了现有技术,以更快,更可靠地进行诊断.

关键词:

在这里,我们可以看到COVID COVID COVID.胸部X射线图像胸部X射线图像深度学习是一种深度学习.图像的分类图像的分类.肺部感染肺部感染视觉变压器视觉变压器

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Last Updated: Jun 19, 2026

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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
传染病诊断传染病诊断传染病诊断

背景情况:

随着COVID-19的爆发,人们越来越需要有效的诊断工具.
像RT-PCR这样的当前方法昂贵且耗时.
胸部X射线 (CXR) 分析对快速检测COVID-19提出了挑战.

研究的目的:

开发一种基于人工智能 (AI) 的诊断工具,使用CXR图像有效检测COVID-19.
与现有方法相比,提高COVID-19诊断的准确性和速度.

主要方法:

利用Kaggle的4035张CXR图像的数据集,包括COVID-19,病毒性肺炎,肺不透明性和健康病例.
用预训练的卷积神经网络 (CNN) 进行员工转移学习:InceptionV3,ResNet50和Xception.
开发了一个新的AI模型,CXR-DNNs,整合了这些先进的技术.

主要成果:

通过集成的AI模型,实现了95%的诊断准确度.
与单独使用ResNet50 (85.5%) 相比,显示了显著更高的准确性.
首次成功地区分了三种不同类型的胸部X射线图像.

结论:

开发的AI工具CXR-DNNs提供了一种高度准确和高效的方法,用于从CXR图像中诊断COVID-19.
这种计算机辅助诊断方法有可能显著提高识别COVID-19感染的速度和可靠性.
该研究展示了人工智能在医学诊断中的力量,特别是在公共卫生危机中.