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Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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X-ray Imaging01:24

X-ray Imaging

5.4K
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...
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Updated: Jun 6, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

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轻质卷积神经网络用于胸部X射线图像的分类.

Chih-Ta Yen1, Chia-Yu Tsao2

  • 1Department of Electrical Engineering, National Taiwan Ocean University, Keelung City, 202301, Taiwan. chihtayen@gmail.com.

Scientific reports
|November 29, 2024
PubMed
概括

一个新的轻量级卷积神经网络 (CNN) 从胸部X射线快速诊断COVID-19. 这种人工智能模型实现了高精度,帮助医疗专业人员快速有效地检测疾病.

科学领域:

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

背景情况:

  • 准确和快速诊断COVID-19对于有效的患者管理和疾病控制至关重要.
  • 胸部X射线成像是诊断呼吸系统疾病的广泛可用和具有成本效益的工具.
  • 医疗图像分析的现有深度学习模型可能是计算密集型,限制实时应用.

研究的目的:

  • 开发一种轻量级和高效的卷积神经网络 (CNN) 架构,以从胸部X射线图像中快速检测COVID-19.
  • 提高AI模型用于COVID-19诊断的速度和减少AI模型的计算要求.
  • 用公开可用的COVID-19放射数据集验证拟议的CNN模型.

主要方法:

  • 开发一种新的CNN架构,包括重新设计的特征提取 (FE) 模块和多尺度特征 (MF) 模块.
  • 在COVID-19放射数据库的多个版本上对CNN模型的培训和验证.
  • 评估模型在三个类别的表现:COVID-19,病毒性/细菌性肺炎和正常的胸部X射线图像.

主要成果:

  • 拟议的CNN在三类分类任务中实现了99.85%的训练准确率和96.28%的验证准确率.
  • 测试组的最佳准确率为COVID-19的96.03%,病毒性/细菌性肺炎的97.10%,正常病例的97.86%.
关键词:
在 COVID-19 疫情中,胸部X射线成像 胸部X射线成像计算机辅助诊断是一种计算机辅助的诊断.卷积神经网络是一种卷积神经网络.轻量级的建筑轻量级的建筑.

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  • 该模型显示,与现有方法相比,计算要求降低,处理速度提高.
  • 结论:

    • 开发的轻量级CNN为COVID-19诊断提供了使用胸部X射线的快速和准确的解决方案.
    • 拟议的架构有效地提取了区分COVID-19,肺炎和正常病例的相关特征.
    • 这种人工智能工具有可能支持医疗专业人员实时进行COVID-19检测的临床决策.