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

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Abstract

Pulmonary fibrosis (PF) is a progressive interstitial lung disease that requires accurate and early detection to improve patient survival and treatment planning. 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. 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.

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