Title:Evaluation of Interstitial Lung Diseases with Deep Learning Method of Two
Major Computed Tomography Patterns
Volume: 20
Author(s): Hüseyin Alper Kiziloğlu*, Emrah Çevik and Kenan Zengin
Affiliation:
- Department of Radiology, Faculty of Medicine, Tokat Gaziosmanpaşa University, Tokat, Türkiye
Keywords:
Artificial intelligence, Interstitial pneumonia, Computed tomography, UIP, NSIP, Deep Learning.
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.
