PDF(Pubmed)
Abstract：
The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoconiosis diagnoses. Three models (Resnet50, Resnet101, and DenseNet) were used for pneumoconiosis classification based on 1250 chest X-ray images. Three experienced and highly qualified physicians read the collected digital radiography images and classified them from category 0 to category III in a double-blinded manner. The results of the 3 physicians in agreement were considered the relative gold standards. Subsequently, 3 models were used to train and test these images and their performance was evaluated using multi-class classification metrics. We used kappa values and accuracy to evaluate the consistency and reliability of the optimal model with clinical typing. The results showed that ResNet101 was the optimal model among the 3 convolutional neural networks. The AUC of ResNet101 was 1.0, 0.9, 0.89, and 0.94 for detecting pneumoconiosis categories 0, I, II, and III, respectively. The micro-average and macro-average mean AUC values were 0.93 and 0.94, respectively. The accuracy and Kappa values of ResNet101 were 0.72 and 0.7111 for quadruple classification and 0.98 and 0.955 for dichotomous classification, respectively, compared with the relative standard classification of the clinic. This study develops a deep learning based model for screening and staging of pneumoconiosis is using chest radiographs. The ResNet101 model performed relatively better in classifying pneumoconiosis than radiologists. The dichotomous classification displayed outstanding performance, thereby indicating the feasibility of deep learning techniques in pneumoconiosis screening.
摘要：
尘肺的诊断是复杂和主观的,导致读数不可避免的可变性。对于没有经验的医生来说尤其如此。为了提高准确性，计算机辅助诊断系统用于更有效的尘肺诊断。三种模型（Resnet50，Resnet101和DenseNet）用于基于1250个胸部X射线图像的尘肺分类。三位经验丰富且高素质的医生阅读收集的数字射线照相图像，并以双盲方式将其从0类分类到III类。同意的3位医生的结果被认为是相对的黄金标准。随后，使用3个模型来训练和测试这些图像，并使用多类分类度量来评估它们的性能。我们使用kappa值和准确性来评估最佳模型与临床分型的一致性和可靠性。结果表明，ResNet101是3种卷积神经网络中的最优模型。ResNet101的AUC分别为1.0、0.9、0.89和0.94，用于检测尘肺类别0、I、II,III,分别。微观平均和宏观平均AUC值分别为0.93和0.94。ResNet101四重分类的准确度和Kappa值分别为0.72和0.7111，二分分类的准确度和Kappa值分别为0.98和0.955，分别,与诊所的相对标准分类相比。这项研究开发了一种基于深度学习的模型，用于使用胸部X光片对尘肺病进行筛查和分期。ResNet101模型在对尘肺进行分类方面比放射科医师表现相对更好。二分法分类表现突出，从而表明深度学习技术在尘肺筛查中的可行性。
