UNASSIGNED: This paper uses the Resnet34 network as a basic classification model. A new cascade lung nodule classification method is proposed to classify lung nodules into 6 classes instead of the traditional 2 or 4 classes. It can effectively classify six different nodule types including ground-glass and solid nodules, benign and malignant nodules, and nodules with predominantly ground-glass or solid components.
UNASSIGNED: In this paper, the traditional multi-classification method and the cascade classification method proposed in this paper were tested using real lung nodule data collected in the clinic. The test results demonstrate that the cascade classification method in this study achieves an accuracy of 80.04%, outperforming the conventional multi-classification approach.
UNASSIGNED: Different from the existing methods for categorizing the benign and malignant nature of lung nodules, the approach presented in this paper can classify lung nodules into 6 categories more accurately. At the same time, This paper proposes a rapid, precise, and dependable approach for classifying six distinct categories of lung nodules, which increases the accuracy categorization compared with the traditional multivariate categorization method.
■本文使用Resnet34网络作为基本分类模型。提出了一种新的级联肺结节分类方法,将肺结节分为6类,而不是传统的2或4类。它可以有效地分类六种不同的结节类型,包括磨玻璃和实性结节,良性和恶性结节,和主要为毛玻璃或固体成分的结节。
■在本文中,传统的多分类方法和本文提出的级联分类方法是使用临床上收集的真实肺结节数据进行测试的。测试结果表明,本研究的级联分类方法达到了80.04%的准确率,优于传统的多分类方法。
■与现有的肺结节良恶性分类方法不同,本文提出的方法可以更准确地将肺结节分为6类。同时,本文提出了一种快速、精确,和可靠的方法来分类六个不同类别的肺结节,与传统的多变量分类方法相比,提高了分类的准确性。