PDF(Pubmed)
Abstract：
UNASSIGNED: Early-stage lung cancer is typically characterized clinically by the presence of isolated lung nodules. Thousands of cases are examined each year, and one case usually contains numerous lung CT slices. Detecting and classifying early microscopic lung nodules is demanding due to their diminutive dimensions and restricted characterization capabilities. Therefore, a lung nodule classification model that performs well and is sensitive to microscopic lung nodules is needed to accurately classify lung nodules.
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.
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.
摘要：
早期肺癌的临床特征通常是存在孤立的肺结节。每年检查数以千计的病例，一个病例通常包含许多肺部CT切片。由于早期微观肺结节的尺寸较小和表征能力有限，因此需要对其进行检测和分类。因此,对肺结节进行准确分类，需要一个性能良好且对微观肺结节敏感的肺结节分类模型。
■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。
■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。
