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Related Concept Videos

X-ray Imaging01:24

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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Deep Neural Networks for Image-Based Dietary Assessment
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X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (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
PubMed
Summary
This summary is machine-generated.

This study developed an AI tool using chest X-rays to detect COVID-19 infections. The new method achieved 95% accuracy, improving upon existing techniques for faster and more reliable diagnosis.

Keywords:
COVIDchest X-ray imagesdeep learningimage classificationlung infectionvision transformer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic highlighted the need for efficient diagnostic tools.
  • Current methods like RT-PCR are costly and time-consuming.
  • Chest X-ray (CXR) analysis presents challenges for rapid COVID-19 detection.

Purpose of the Study:

  • To develop an artificial intelligence (AI) based diagnostic tool for efficient COVID-19 detection using CXR images.
  • To improve the accuracy and speed of COVID-19 diagnosis compared to existing methods.

Main Methods:

  • Utilized a dataset of 4035 CXR images from Kaggle, including COVID-19, viral pneumonia, pulmonary opacity, and healthy cases.
  • Employed transfer learning with pre-trained convolutional neural networks (CNNs): InceptionV3, ResNet50, and Xception.
  • Developed a novel AI model, CXR-DNNs, integrating these advanced techniques.

Main Results:

  • Achieved a diagnostic accuracy of 95% with the integrated AI model.
  • Demonstrated significantly higher accuracy compared to using ResNet50 alone (85.5%).
  • Successfully distinguished between three different types of chest X-ray images for the first time.

Conclusions:

  • The developed AI tool, CXR-DNNs, offers a highly accurate and efficient method for COVID-19 diagnosis from CXR images.
  • This computer-assisted diagnostic approach has the potential to significantly enhance the speed and reliability of identifying COVID-19 infections.
  • The study showcases the power of AI in medical diagnostics, particularly for public health crises.