Explainable pulmonary fibrosis detection using edge-strengthened dilated holistic edge detection-based lung segmentation and ResNet-V2 classification.

Introduction: Pulmonary fibrosis (PF) is a progressive interstitial lung disease that requires accurate and early detection to improve patient survival and treatment planning. Methods: This study proposes an explainable deep learning framework for pulmonary fibrosis detection from chest X-ray images by integrating an edge-strengthened dilated holistic edge detection (ES-D-HED) segmentation network with a fine-tuned ResNet152V2 classification model. Unlike the conventional HED-based approaches, the proposed ES-D-HED architecture incorporates an additional dilated intermediate-output branch and enhanced multi-scale edge fusion to improve contextual boundary modeling and fibrosis-related structural edge continuity. The framework combines edge-aware lung segmentation, fibrosis classification, and Grad-CAM-based explainability to provide interpretable clinical decision support. The model was evaluated on a curated subset of the publicly available NIH Chest X-ray dataset using patient-level five-fold cross-validation. Since the NIH dataset does not contain fibrosis segmentation masks, representative PF regions were retrospectively annotated by a clinical expert radiologist for quantitative validation. Results and Discussion: Experimental results demonstrated a classification accuracy of 98.6%, sensitivity of 98.0%, specificity of 99.2%, and F1-score of 98.5%. The proposed segmentation model achieved Dice similarity coefficients of 0.904 for normal lung segmentation and 0.843 for PF region segmentation, indicating strong structural alignment with expert annotations. Grad-CAM visualization also confirmed that the model successfully identified abnormal lung areas associated with fibrosis. The proposed framework shows the potential application of edge-strengthened explainable deep learning in the PF screening system based on chest X-ray imaging. Future studies will involve the classification of multi-class interstitial lung disease, grading the severity of fibrosis, and the multi-center clinical validation for real-world applicability.