Deformable Dual-Path Networks for Chronic Obstructive Pulmonary Disease Staging in CT Images

Chronic obstructive pulmonary disease (COPD) is a respiratory disease that progresses over time and can significantly affect a person’s quality of life. Our proposed method for assessing the severity of COPD involves using lung computed tomography (CT) scans and a deep learning model called Deformable Dual-Path Networks. This model incorporates a side path that is densely connected to learn as many pathological features as possible. Our study provides a methodological idea for making full use of the results of vascular and airway tree segmentation in the diagnosis and monitoring of COPD patients, which can provide a reference for future researchers. We have created a network that uses chest CT scans to classify patients with COPD. Our method was tested on a dataset of 70 patients, and the results showed that it performs better than existing methods. This approach has the potential to enhance the diagnosis and monitoring of COPD patients. In summary, our method shows promise for improving the accuracy and efficiency of COPD severity assessment through medical imaging.