Multi-scale pyramid spatial atrous channel attention enhanced ResNet50V2 for explainable pulmonary fibrosis diagnosis

Introduction Pulmonary fibrosis is a progressive interstitial lung disease where delayed or erroneous diagnosis may result in severe clinical consequences. While chest CT imaging serves as the principal method for evaluation, manual interpretation is time-consuming and susceptible to observer variability. Moreover, most modern technologies operate as “black boxes,” resulting in diminished transparency in clinical decision-making. To overcome these limitations, this study proposes a novel Pyramid Spatial Atrous Channel Attention (PSACA)-based feature enhancement module integrated into the ResNet50V2 deep transfer learning framework for automated pulmonary fibrosis classification from chest CT images. Methods The proposed model integrates with PSACA into the ResNet50V2 backbone, prioritizing spatial attention before channel attention, with Atrous Spatial Pyramid Pooling added between them to enhance discriminative feature representation in disease-relevant regions. A multi-level pyramid model, which is designed on parallel dilated convolutions, provides hierarchical contextual detail by incrementally increasing receptive fields, providing the ability to model robustly local-to-global pulmonary patterns. By combining spatial attention, atrous spatial pyramid pooling, and channel attention, the fibrosis-relevant regions and informative feature channels are selectively enhanced and lead to a better detection of normal and fibrotic lung tissues. This combined block is embedded in Stage 3 and Stage 4 of ResNet50V2 to maximize high-level fibrosis representation while keeping computational complexity low. Results and discussion Experimental evaluation on a balanced CT dataset demonstrates that the proposed method achieves a classification accuracy of 99.83%, sensitivity 99.93%, specificity 99.72%, F1-score 99.83%, and precision 99.72%. Grad-CAM++ explainability yields clinically meaningful heatmaps that highlight fibrotic abnormalities, enhancing radiologist confidence and interpretability. Overall, the proposed architecture presents a novel multi-scale attention mechanism designed for pulmonary fibrosis, providing enhanced feature discrimination, greater localization, and enhanced clinical explainability.