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Related Concept Videos

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

868
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
868

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Related Experiment Video

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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COVID-19 disease severity assessment using CNN model.

Emrah Irmak1

  • 1Electrical-Electronics Engineering Department Alanya Alaaddin Keykubat University Alanya Antalya Turkey.

IET Image Processing
|July 7, 2021
PubMed
Summary

This study introduces a convolutional neural network (CNN) model for classifying COVID-19 severity using chest X-rays. The automated CNN achieved 95.52% accuracy in distinguishing between mild, moderate, severe, and critical cases.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The COVID-19 pandemic has overwhelmed healthcare systems, necessitating efficient patient stratification.
  • Limited clinical resources require accurate assessment of disease severity for effective patient management.
  • Chest X-rays are crucial for diagnosing COVID-19 but manual interpretation can be time-consuming.

Purpose of the Study:

  • To develop and validate an automated Convolutional Neural Network (CNN) model for COVID-19 severity classification.
  • To assess the effectiveness of the CNN model in categorizing patients into four severity levels: mild, moderate, severe, and critical.
  • To establish a novel, automated approach for rapid COVID-19 severity assessment using chest X-ray images.

Main Methods:

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  • A novel Convolutional Neural Network (CNN) architecture was designed and implemented for image analysis.
  • The CNN model was trained and tested on a large dataset of chest X-ray images from COVID-19 patients.
  • Hyper-parameters of the CNN model were automatically tuned using a grid search optimization technique.

    • Main Results:

    • The automated CNN model achieved an average accuracy of 95.52% in classifying COVID-19 severity.
    • Experimental results demonstrated the model's effectiveness on a substantial number of chest X-ray images.
    • The study successfully categorized patients into four distinct severity classes (mild, moderate, severe, critical).

    Conclusions:

    • The proposed CNN model offers an effective and automated solution for COVID-19 severity assessment.
    • This approach can aid clinicians and radiologists in managing patient flow and treatment decisions.
    • This study represents a significant advancement in automated disease severity classification using medical imaging.