Khomkham Banphatree, Lipikorn Rajalida
Machine Intelligence and Multimedia Information Technology Laboratory (MIMIT), Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Diagnostics (Basel). 2022 Jun 26;12(7):1552. doi: 10.3390/diagnostics12071552.
Lung cancer is a deadly disease with a high mortality rate. Endobronchial ultrasonography (EBUS) is one of the methods for detecting pulmonary lesions. Computer-aided diagnosis of pulmonary lesions from images can help radiologists to classify lesions; however, most of the existing methods need a large volume of data to give good results. Thus, this paper proposes a novel pulmonary lesion classification framework for EBUS images that works well with small datasets. The proposed framework integrates the statistical results from three classification models using the weighted ensemble classification. The three classification models include the radiomics feature and patient data-based model, the single-image-based model, and the multi-patch-based model. The radiomics features are combined with the patient data to be used as input data for the random forest, whereas the EBUS images are used as input data to the other two CNN models. The performance of the proposed framework was evaluated on a set of 200 EBUS images consisting of 124 malignant lesions and 76 benign lesions. The experimental results show that the accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve are 95.00%, 100%, 86.67%, 92.59%, 100%, and 93.33%, respectively. This framework can significantly improve the pulmonary lesion classification.
肺癌是一种死亡率很高的致命疾病。支气管内超声检查（EBUS）是检测肺部病变的方法之一。通过图像对肺部病变进行计算机辅助诊断有助于放射科医生对病变进行分类；然而，大多数现有方法需要大量数据才能取得良好效果。因此，本文提出了一种适用于小数据集的新型EBUS图像肺部病变分类框架。所提出的框架使用加权集成分类法整合了三个分类模型的统计结果。这三个分类模型包括基于影像组学特征和患者数据的模型、基于单图像的模型以及基于多补丁的模型。影像组学特征与患者数据相结合，用作随机森林的输入数据，而EBUS图像则用作其他两个卷积神经网络（CNN）模型的输入数据。在所提出的框架的性能在一组由124个恶性病变和76个良性病变组成的200张EBUS图像上进行了评估。实验结果表明，准确率、灵敏度、特异度、阳性预测值、阴性预测值和曲线下面积分别为95.00%、100%、86.67%、92.59%、100%和93.33%。该框架可以显著提高肺部病变分类的效果。
