Hüseyin Alper Kiziloğlu, Emrah Çevik, Kenan Zengin
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Evaluation of Interstitial Lung Diseases with Deep Learning Method of Two Major Computed Tomography Patterns.
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.
期刊介绍：
Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques.
The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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