Department of Software Engineering, Foundation University Islamabad, Islamabad, Pakistan.
Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan.
Sci Rep. 2019 Mar 21;9(1):4989. doi: 10.1038/s41598-019-41510-9.
Lung cancer is considered more serious among other prevailing cancer types. One of the reasons for it is that it is usually not diagnosed until it has spread and by that time it becomes very difficult to treat. Early detection of lung cancer can significantly increase the chances of survival of a cancer patient. An effective nodule detection system can play a key role in early detection of lung cancer thus increasing the chances of successful treatment. In this research work, we have proposed a novel classification framework for nodule classification. The framework consists of multiple phases that include image contrast enhancement, segmentation, optimal feature extraction, followed by employment of these features for training and testing of Support Vector Machine. We have empirically tested the efficacy of our technique by utilizing the well-known Lung Image Consortium Database (LIDC) dataset. The empirical results suggest that the technique is highly effective for reducing the false positive rates. We were able to receive an impressive sensitivity rate of 97.45%.
肺癌在其他常见癌症类型中被认为更为严重。其中一个原因是，通常在肺癌扩散后才被诊断出来，而此时治疗难度很大。早期发现肺癌可以显著提高癌症患者的生存机会。一个有效的结节检测系统可以在肺癌的早期检测中发挥关键作用，从而提高成功治疗的机会。在这项研究工作中，我们提出了一种新的结节分类框架。该框架由多个阶段组成，包括图像对比度增强、分割、最优特征提取，然后利用这些特征进行支持向量机的训练和测试。我们通过利用著名的肺部图像联盟数据库 (LIDC) 数据集，对我们的技术的有效性进行了实证测试。实验结果表明，该技术对降低假阳性率非常有效。我们能够获得令人印象深刻的 97.45%的灵敏度。
