Anatomic Feature Fusion Model for Diagnosing Calcified Pulmonary Nodules on Chest X-Ray
This work addresses the challenge of inconsistent physician interpretation and overlapping anatomical elements in diagnosing benign nodules on chest X-rays, representing an incremental improvement in medical imaging.
This study tackled the problem of accurately diagnosing calcified pulmonary nodules on chest X-rays, which is crucial for early treatment and avoiding unnecessary procedures, by developing a classification model that fused features from raw and structure-suppressed images to reduce anatomical interference, achieving an accuracy of 86.52% and an AUC of 0.8889.
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.