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Related Concept Videos

Pneumonia I: Introduction01:30

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Pneumonia is an acute respiratory infection that targets the lungs, specifically the alveoli. These tiny air sacs, essential for oxygen exchange, become engorged with pus and fluid, severely hindering breathing, decreasing oxygen absorption, and causing significant pain and discomfort during respiration.
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Related Experiment Video

Updated: Aug 27, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence.

Mohammed J Abdulaal1,2, Ibrahim M Mehedi1,2, Abdullah M Abusorrah1

  • 1Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah, Saudi Arabia.

Contrast Media & Molecular Imaging
|September 30, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient deep learning model for detecting Coronavirus 2019 (COVID-19) from chest X-rays. The developed convolutional neural network achieved 99.6% accuracy in identifying COVID-19 cases.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Coronavirus 2019 (COVID-19) is a global pandemic with significant mortality.
  • Accurate and efficient diagnostic tools are crucial for managing the pandemic.
  • Deep learning offers potential for automated analysis of medical images.

Purpose of the Study:

  • To develop and evaluate an efficient deep semantic segmentation network for COVID-19 detection using chest X-rays.
  • To optimize a custom convolutional neural network model for improved accuracy and efficiency.
  • To assess the performance of image enhancement and data augmentation techniques in COVID-19 diagnosis.

Main Methods:

  • Utilized dynamic adaptive histogram equalization for image enhancement.
  • Applied data augmentation techniques to increase dataset variability.
  • Developed a custom convolutional neural network (CNN) by integrating and refining pretrained ImageNet models.
  • Compared multiple model variations to select the most efficient and accurate configuration.

Main Results:

  • The proposed model achieved a high accuracy of 99.6% for COVID-19 detection.
  • The model demonstrated a strong performance with an area under the curve (AUC) of 0.996.
  • Optimized model complexity and memory efficiency through iterative trimming of well-performing components.

Conclusions:

  • The developed customized smart convolutional neural network is highly effective for COVID-19 detection from chest X-rays.
  • The integration of image enhancement and data augmentation significantly contributes to diagnostic accuracy.
  • This deep learning approach shows promise for rapid and reliable screening of COVID-19.