Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging

TL;DR

This paper introduces a novel weighted broad learning system that leverages GLCM texture features to improve the accuracy of occupational pneumoconiosis staging from chest X-ray images, especially under imbalanced data conditions.

Contribution

It proposes a new OP staging model combining GLCM features with WBLS to effectively handle data imbalance, enhancing classification accuracy.

Findings

Outperforms existing classifiers on imbalanced datasets

Effective texture feature extraction using GLCM

Improved staging accuracy demonstrated on hospital data

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.