Evaluation of Interstitial Lung Diseases with Deep Learning Method of Two Major Computed Tomography Patterns

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: The success of the VGG16 model in the test phase was 95.02% accuracy. 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.