Abstract
This study presents a methodology for the automated detection and quantification of pulmonary emphysema from low-dose chest computed tomography (CT) scans. As a morphological subtype of chronic obstructive pulmonary disease (COPD), emphysema can be accurately assessed on CT imaging. Our approach utilizes a pre-trained ResNet152 model to extract high-dimensional feature embeddings (2048 dimensions) from mid-lung patches. Patients were automatically categorized based on the percentage of low attenuation areas (LAA%) below –950 Hounsfield units (HU), a standard measure for emphysema severity. The extracted feature embeddings were subsequently analyzed using statistical methods and logistic regression to identify key discriminative and interpretable features. A logistic regression model, trained on the top 20 most salient features, achieved a high level of performance, with an Area Under the Curve (AUC) of 0.94 and an Average Precision (AP) of 0.87 on a balanced dataset of 90 subjects. Furthermore, the selected features exhibited a strong correlation with LAA%, demonstrating their utility for regression-based severity assessment. The findings confirm the viability of using pre-trained deep embeddings for transparent and reproducible emphysema screening. This method avoids the need for extensive end-to-end model retraining, making it highly adaptable for integration into existing clinical CT analysis workflows. Keywords: Emphysema, low-dose CT, deep learning, ResNet, feature embeddings, explainable AI, COPD.