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Related Concept Videos

Imaging Studies for Cardiovascular System III: X-Ray01:20

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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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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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Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images.

Shubham Agrawal1, Venkatesh Honnakasturi1, Madhumitha Nara1

  • 1Department of Information Technology, National Institute of Technology Karnataka, Surathkal, 575025 India.

SN Computer Science
|April 24, 2023
PubMed
Summary

This study demonstrates that the ResNet50 model effectively detects COVID-19 from X-rays, achieving high accuracy. The modified ResNet50 model can also differentiate between COVID-19 and pneumonia, aiding in diagnosis.

Keywords:
Chest X-rayClassificationCoronavirus (COVID-19)Deep learningGrad-CAMHeatmapLIME

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic necessitated rapid and accurate diagnostic tools.
  • Shortages in testing equipment and expert personnel highlighted the need for automated detection methods.
  • Radiographic imaging, specifically X-rays, offers a potential avenue for COVID-19 diagnosis.

Purpose of the Study:

  • To evaluate and compare various deep learning models for detecting COVID-19 from chest X-ray images.
  • To develop and validate a modified ResNet50 model for both binary (COVID-19 vs. No COVID-19) and multi-class (COVID-19 vs. No COVID-19 vs. Pneumonia) classification.
  • To interpret the model's decision-making process using explainability techniques like LIME and Grad-CAM.

Main Methods:

  • Evaluation of classic convolutional neural network (CNN) architectures including VGG19, ResNet50, MobileNetV2, InceptionV3, Xception, and DenseNet121.
  • Comparison with specialized COVID-19 detection models like DarkCOVIDNet and COVID-Net.
  • Development of a modified ResNet50 model and assessment of its performance on binary and multi-class classification tasks.
  • Application of LIME and Grad-CAM for model interpretability and visualization of classification features.

Main Results:

  • ResNet50 emerged as the top-performing model among the evaluated classic architectures.
  • The modified ResNet50 model achieved a binary classification accuracy of 99.20% for COVID-19 detection.
  • The modified ResNet50 model achieved an 86.13% accuracy in multi-class classification, distinguishing COVID-19 from pneumonia.
  • LIME provided better interpretability through contour maps compared to Grad-CAM's heatmaps, highlighting regions indicative of COVID-19.

Conclusions:

  • The ResNet50 architecture, particularly the modified version, demonstrates significant potential for accurate COVID-19 detection from X-rays.
  • The model's ability to differentiate between COVID-19 and pneumonia is crucial for clinical decision-making.
  • Explainability methods confirm the model's focus on relevant lung regions, supporting its generalization capabilities.
  • The proposed model is intended for free deployment to aid global diagnostic efforts.