## 1 Introduction  
Occupational lung diseases, particularly among blasting workers, are increasingly linked to particulate exposure and blast wave effects. According to data from the Occupational Safety and Health Administration (OSHA) [1], prolonged occupational exposure to respirable crystalline silica (RCS) among miners has been documented to reach intensity levels 4--2400 times higher than general industrial permissible exposure limits (PELs). Epidemiological analyses [2] reveal a significantly increased detection rate of pulmonary nodules in dust-exposed populations (including miners) compared to the general population. Moreover, mortality rates in miners with concurrent tuberculosis infection are significantly higher compared to general patients presenting with tuberculosis alone. Early detection of benign/malignant pulmonary nodules in this population is critical but hindered by limited access to expert radiologists and underdeveloped healthcare infrastructure in remote mining areas, highlighting an urgent need for automated diagnostic tools. Existing AI tools, however, struggle with noise from irregular lesion morphologies caused by heterogeneous dust deposition patterns. Radiomics-based classification methods are suitable for small-scale datasets and are interpretable and operational, as precision medicine often relies on quantitative data. However, most extracted features, such as tumor shape, intensity, and texture, are manually designed. These shallow, low-dimensional features may fail to capture the full heterogeneity of medical images, limiting the accuracy and robustness of nodule prediction [3]. In contrast, deep learning models automatically extract high-dimensional features but require large datasets and time-consuming expert annotations. Moreover, the interpretability of end-to-end models remains a significant challenge. As model structures become increasingly complex, this study pioneers an occupational health-oriented, transparent, and resource-efficient analytics paradigm for dust-related pulmonary screening. We propose an interpretable and lightweight ensemble learning model leveraging multi-dimensional features, with three domain-specific advancements:  
* Morphological Purification: Unlike conventional methods that extract ROIs based on labeled coordinates, which may include noise such as small blood vessels or spicules caused by explosive residue deposition and intermittent dust clouds, we employ a more systematic preprocessing workflow. This includes morphological operations, multiple fillings, convex hull calculations, label consensus merging, and random rotations, ensuring the extraction of cleaner and more representative ROIs. * Interpretability by Design: We integrate clinical annotated features, radiomics features, and deep features into an interpretable ensemble model. Feature weight maps and normalized mutual information heatmaps ensure the transparency of model decisions, providing actionable insights for clinicians. * Resource-Accuracy Co-Optimization: By shifting computational burdens from hardware-dependent deep architectures to precision-enhanced preprocessing, our approach achieves SOTA accuracy using only clinical-grade equipment. This makes it deployable in resource-constrained settings without sacrificing diagnostic rigor. This paper is organized as follows: Section 2 provides a comprehensive discussion on the development of prediction techniques for diseases such as pulmonary nodules, summarizing the value of various methods and their existing limitations. In Section 2.2, we focus on the challenges associated with medical imaging of patients in specialized industries, the shortcomings in handling image data in most related studies, and the difficulties in applying these techniques in resource-constrained environments. In Section 3, we propose an applicable framework that covers the entire process from data preprocessing and multi-dimensional feature extraction to auxiliary diagnosis, effectively addressing some of the issues discussed in the related work.