Department of Engineering Sciences, Istanbul University, 34320 Avcılar, Istanbul, Turkey.
Comput Math Methods Med. 2013;2013:148363. doi: 10.1155/2013/148363. Epub 2013 Jun 25.
Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).
早期发现肺部结节对于肺癌的诊断和治疗至关重要。本研究提出了一种新的基于 CT 图像的肺部结节分类方法，该方法使用混合特征。为提出的系统引入了四种不同的方法。使用各种分类器评估整体检测性能。使用标准指标将结果与文献中的类似技术进行比较。使用混合特征的方法的分类准确率为 90.7%（89.6%的灵敏度和 87.5%的特异性）。
