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Related Concept Videos

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...
344
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

630
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...
630

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Related Experiment Video

Updated: Nov 8, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.4K

CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR.

Maram Mahmoud A Monshi1, Josiah Poon2, Vera Chung2

  • 1School of Computer Science, The University of Sydney, Camperdown, NSW, 2006, Australia; Department of Information Technology, Taif University, Taif, 26571, Saudi Arabia.

Computers in Biology and Medicine
|April 18, 2021
PubMed
Summary

This study optimized Artificial Intelligence (AI) models for faster COVID-19 detection using Chest X-rays (CXRs). The developed CovidXrayNet achieved high accuracy, aiding in pandemic screening and patient isolation.

Keywords:
COVID-19Chest X-RayConvolutional neural networkData augmentationHyperparameters

Related Experiment Videos

Last Updated: Nov 8, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.4K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • The COVID-19 pandemic necessitates rapid and accurate patient screening for isolation and treatment.
  • Chest X-ray (CXR) imaging is a key diagnostic tool, and Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), can enhance its efficiency.
  • Optimizing AI models for CXR analysis can significantly accelerate the COVID-19 diagnostic process.

Purpose of the Study:

  • To optimize data augmentation and CNN hyperparameters for improved COVID-19 detection from CXRs.
  • To develop and evaluate a novel AI model, CovidXrayNet, for accurate COVID-19 classification.
  • To enhance the accuracy of existing CNN architectures like VGG-19 and ResNet-50 for COVID-19 detection.

Main Methods:

  • Data augmentation and CNN hyperparameters were optimized to maximize validation accuracy.
  • Popular CNN architectures (VGG-19, ResNet-50) were fine-tuned using optimized parameters.
  • A new model, CovidXrayNet, was developed based on the EfficientNet-B0 architecture and optimization findings.
  • CovidXrayNet was evaluated on two datasets: a custom COVIDcxr dataset and the benchmark COVIDx dataset.

Main Results:

  • Optimization led to accuracy increases of 11.93% for VGG-19 and 4.97% for ResNet-50.
  • CovidXrayNet achieved a state-of-the-art accuracy of 95.82% on the COVIDx dataset.
  • The model demonstrated high performance in a three-class classification task (COVID-19, normal, pneumonia) within 30 training epochs.

Conclusions:

  • Optimized AI models, particularly CovidXrayNet, show significant potential for accurate and rapid COVID-19 detection from CXRs.
  • The proposed CovidXrayNet model offers a reliable tool for clinical screening, aiding in pandemic management.
  • Public availability of the model, dataset, and experiments facilitates further research and development in AI-driven medical diagnostics.