# Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging Source: [Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:29:35.891Z Authors: Kaiguang Yang, Yeping Wang, Qianhao Luo, Xin Liu, Weiling Li Published: 2022-08-13T09:31:25.000Z ## Abstract Occupational pneumoconiosis (OP) staging is a vital task concerning the lung healthy of a subject. The staging result of a patient is depended on the staging standard and his chest X-ray. It is essentially an image classification task. However, the distribution of OP data is commonly imbalanced, which largely reduces the effect of classification models which are proposed under the assumption that data follow a balanced distribution and causes inaccurate staging results. To achieve accurate OP staging, we proposed an OP staging model who is able to handle imbalance data in this work. The proposed model adopts gray level co-occurrence matrix (GLCM) to extract texture feature of chest X-ray and implements classification with a weighted broad learning system (WBLS). Empirical studies on six data cases provided by a hospital indicate that proposed model can perform better OP staging than state-of-the-art classifiers with imbalanced data. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()