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Updated: Nov 4, 2025

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DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning.

Amel Imene Hadj Bouzid1, Said Yahiaoui1, Anis Lounis1

  • 1CERIST, Research Center on Scientific and Technical Information, Algiers, Algeria.

Studies in Health Technology and Informatics
|May 27, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning models accurately classify COVID-19 positive patients from healthy individuals using lung CT scans, achieving over 92% accuracy. A computer-aided diagnosis web application is proposed for rapid patient management.

Keywords:
CNNCT-scanClassificationCovid-19Deep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • The COVID-19 pandemic necessitates efficient diagnostic tools.
  • Lung CT scans are crucial for diagnosis but can overwhelm radiologists.
  • Automated image interpretation is needed to support clinical workflows.

Purpose of the Study:

  • To develop and evaluate deep learning models for classifying COVID-19 positive patients from healthy individuals using lung CT scans.
  • To assess the generalizability of the developed models across different datasets.
  • To propose a computer-aided diagnosis (CAD) web application for COVID-19 detection.

Main Methods:

  • Collected and utilized four publicly available datasets of lung CT scans.
  • Trained and tested convolutional neural networks (CNNs) on various data distributions.
  • Employed Grad-CAM and Fast-CAM visualization techniques to interpret model predictions.
  • Developed a computer-aided diagnosis web application.

Main Results:

  • Achieved over 92% accuracy in classifying COVID-19 positive and healthy patients across two different data distributions.
  • Demonstrated the generalizability of the CNN models.
  • Successfully visualized model decision-making processes using Grad-CAM and Fast-CAM.

Conclusions:

  • Deep learning models show high accuracy and generalizability for COVID-19 detection from lung CT scans.
  • The proposed CAD web application can aid in rapid patient management.
  • The developed DL tool has the potential for integration into clinical settings to assist in COVID-19 diagnosis.