  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.
