论文标题
不纠正的加权板学习系统，用于准确的职业性肺炎分期
Incoporating Weighted Board Learning System for Accurate Occupational Pneumoconiosis Staging
论文作者
论文摘要
职业性肺炎（OP）分期是有关受试者肺部健康的重要任务。患者的分期结果取决于分期标准和他的胸部X射线。它本质上是图像分类任务。但是，OP数据的分布通常是不平衡的，这在很大程度上降低了分类模型的效果，这是在数据遵循平衡分布并导致分阶段不准确的假设下提出的。为了实现准确的运行阶段，我们提出了一个能够在这项工作中处理不平衡数据的OP登台模型。所提出的模型采用灰度合作矩阵（GLCM）来提取胸部X射线的纹理特征，并使用加权宽学习系统（WBLS）实现分类。对医院提供的六个数据案例的经验研究表明，提出的模型比具有不平衡数据的最先进的分类器可以执行更好的OP分期。
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.
