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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

Interstitial lung diseases (ILD) encompass various disorders characterized by inflammation and/or fibrosis in the lung interstitium. These conditions produce distinct patterns in High-Resolution Computed Tomography (HRCT).

Objective

We employ a deep learning method to diagnose the most commonly encountered patterns in ILD differentially.

Materials and Methods

Patients were categorized into usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), and normal lung parenchyma groups. VGG16 and VGG19 deep learning architectures were utilized. 85% of each pattern was used as training data for the artificial intelligence model. The models were then tasked with diagnosing the patterns in the test dataset without human intervention. Accuracy rates were calculated for both models.

Results

1 The success of the VGG16 model in the test phase was 95.02% accuracy. 2 Using the same data, 98.05% accuracy results were obtained in the test phase of the VGG19 model.

Conclusion

Deep Learning models showed high accuracy in distinguishing the two most common patterns of ILD.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-06-26

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/content/journals/cmir/10.2174/0115734056279295231229095436
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Evaluation of Interstitial Lung Diseases with Deep Learning Method of Two Major Computed Tomography Patterns
Curr. Med. Imaging 20, e15734056279295 (2024); https://doi.org/10.2174/0115734056279295231229095436
/content/journals/cmir/10.2174/0115734056279295231229095436
/content/journals/cmir/10.2174/0115734056279295231229095436
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  • Article Type:     Research Article
Keyword(s): Artificial intelligence; Computed tomography; Deep Learning; Interstitial pneumonia; NSIP; UIP

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