Anatomic Feature Fusion Model for Diagnosing Calcified Pulmonary Nodules on Chest X-Ray

TL;DR

This paper introduces a fusion-based model that improves the accuracy of diagnosing calcified pulmonary nodules on chest X-rays by combining features from raw and structure-suppressed images, outperforming previous methods.

Contribution

The study presents a novel feature fusion approach that enhances calcification classification accuracy on chest X-rays, addressing challenges of structural interference and interpretation variability.

Findings

Achieved 86.52% accuracy in calcification diagnosis

Attained an AUC of 0.8889, outperforming raw image models

Reduced structural interference in feature extraction

Abstract

Accurate and timely identification of pulmonary nodules on chest X-rays can differentiate between life-saving early treatment and avoidable invasive procedures. Calcification is a definitive indicator of benign nodules and is the primary foundation for diagnosis. In actual practice, diagnosing pulmonary nodule calcification on chest X-rays predominantly depends on the physician's visual assessment, resulting in significant diversity in interpretation. Furthermore, overlapping anatomical elements, such as ribs and spine, complicate the precise identification of calcification patterns. This study presents a calcification classification model that attains strong diagnostic performance by utilizing fused features derived from raw images and their structure-suppressed variants to reduce structural interference. We used 2,517 lesion-free images and 656 nodule images (151 calcified nodules and 550 non-calcified nodules), all obtained from Ajou University Hospital. The suggested model attained an accuracy of 86.52% and an AUC of 0.8889 in calcification diagnosis, surpassing the model trained on raw images by 3.54% and 0.0385, respectively.