Multi-Class Diagnosis of Pulmonary Hypertension in the Presence of COPD via CT Imaging and Transformer–CNN Fusion

Pulmonary hypertension (PH) is a life-threatening condition that often coexists with chronic obstructive pulmonary disease (COPD), yet few diagnostic studies explicitly account for this comorbidity. When diagnosing PH from computed tomography (CT) images using deep learning models, irrelevant visual components commonly present in the imaging field may introduce background noise and hinder accurate assessment of the pulmonary artery. To tackle these issues, we construct a CT-based dataset comprising four categories: PH with COPD, PH only, COPD only, and controls.We propose a novel classification framework that integrates EfficientNet-B0 and a modified Swin Transformer branch enhanced with Multiscale Dynamic Swin Transformer (MDST) blocks. To guide the model’s attention toward diagnostically relevant regions, we introduce a Pulmonary Artery Region Masking (PARM) strategy that adaptively isolates the pulmonary artery and suppresses background noise. Experimental results show that the PARM-guided EfficientNet combined with the Multiscale Dynamic Swin Transformer (Eff-MDST) achieves an accuracy of 0.8569, a macro-precision of 0.8634, a macro-recall of 0.8601, and a macro-F1 score of 0.8570. These results clearly outperform conventional Convolutional Neural Networks (CNNs) as well as baseline Transformer architectures, with an overall accuracy improvement of at least 0.0147. These findings highlight the potential of combining anatomical priors with Transformer-based architectures to advance CT-based PH diagnosis in the presence of COPD.