Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists use automated tools for more precise opinion. Automated detection of the affected lung nodules is complicated because of the shape similarity among healthy and unhealthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer effectively. In this article, we have proposed a framework to precisely detect lungs cancer to classify the benign and malignant nodules. The proposed framework is tested using the subset of the publicly available dataset, i.e., the Lung Image Database Consortium image collection (LIDC-IDRI). We applied filtering and noise removal in the pre-processing phase. Furthermore, the adaptive thresholding technique (OTSU) and the semantic segmentation are used to accurately detect the unhealthy lung nodules. Overall, 13 nodules features have extracted using principal components analysis algorithm. In addition, four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are employed for the experimentation. Empirical analysis shows that the proposed system outperformed other techniques and provides 99.23% accuracy using a logit boost classifier.

如果不早期诊断肺癌，则它是一种致命疾病。然而，由于其结节的形状和大小，早期发现肺癌是一项艰巨的任务。放射科医生使用自动化工具获得更准确的意见。由于健康和不健康组织之间的形状相似性，自动检测受影响的肺结节很复杂。多年来，已经开发了一些专家系统，可以帮助放射科医生有效地诊断肺癌。在本文中，我们提出了一个框架来精确检测肺癌，以对良性和恶性结节进行分类。使用公开可用数据集的子集，即肺图像数据库联盟图像集合（LIDC-IDRI），对提出的框架进行了测试。我们在预处理阶段应用了滤波和噪声消除。此外，自适应阈值技术（OTSU）和语义分割用于准确检测不健康的肺结节。总体而言，使用主成分分析算法提取了13个结节特征。另外，基于分类性能选择四个最佳特征。在分类阶段，将9种不同的分类器用于实验。实证分析表明，提出的系统优于其他技术，并使用对数提升分类器可提供99.23％的准确性。在分类阶段，将9种不同的分类器用于实验。实证分析表明，提出的系统优于其他技术，并使用对数提升分类器可提供99.23％的准确性。在分类阶段，将9种不同的分类器用于实验。实证分析表明，提出的系统优于其他技术，并使用对数提升分类器可提供99.23％的准确性。