ReAcc_MF: Multimodal Fusion Model with Resource-Accuracy Co-Optimization for Screening Blasting-Induced Pulmonary Nodules in Occupational Health

Occupational health monitoring in demolition environments requires precise detection of blast-dust-induced pulmonary pathologies. However, it is often hindered by challenges such as contaminated imaging biomarkers, limited access to medical resources in mining areas, and opaque AI-based diagnostic models. This study presents a novel computational framework that combines industrial-grade robustness with clinical interpretability for the diagnosis of pulmonary nodules. We propose a hybrid framework that integrates morphological purification techniques (multi-step filling and convex hull operations) with multi-dimensional features fusion (radiomics + lightweight deep features). To enhance computational efficiency and interpretability, we design a soft voting ensemble classifier, eliminating the need for complex deep learning architectures. On the LIDC-IDRI dataset, our model achieved an AUC of 0.99 and an accuracy of 0.97 using standard clinical-grade hardware, outperforming state-of-the-art (SOTA) methods while requiring fewer computational resources. Ablation studies, feature weight maps, and normalized mutual information heatmaps confirm the robustness and interpretability of the model, while uncertainty quantification metrics such as the Brier score and Expected Calibration Error (ECE) better validate the model’s clinical applicability and prediction stability. This approach effectively achieves resource-accuracy co-optimization, maintaining low computational costs, and is highly suitable for resource-constrained clinical environments. The modular design of our framework also facilitates extensions to other medical imaging domains without the need for high-end infrastructure.